{"cells":[{"cell_type":"markdown","metadata":{"id":"845368C52E49432B8D8076F774D813B0","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["# 电商产品评论数据情感分析  \n","\n","\n","针对用户在电商平台上留下的评论数据，对其进行分词、词性标注和去除停用词等文本预处理。基于预处理后的数据进行情感分析，并使用LDA主题模型提取评论关键信息，以了解用户的需求、意见、购买原因及产品的优缺点等，最终提出改善产品的建议  \n","\n","----  \n","\n","## 数据预处理  \n","\n","### 评论去重  \n","\n","一些电商平台为了避免一些客户长时间不进行评论，往往会设置一道程序，如果用户超过规定的时间仍然没有做出评论，系统就会自动替客户做出评论，这类数据显然没有任何分析价值。由语言的特点可知，在大多数情况下，不同购买者之间的有价值的评论是不会出现完全重复的，如果不同购物者的评论完全重复，那么这些评论一般都是毫无意义的。为了存留更多的有用语料，本节针对完全重复的语料下手，仅删除完全重复部分，以确保保留有用的文本评论信息。"]},{"cell_type":"code","execution_count":1,"metadata":{"collapsed":false,"id":"DAF242D3C9D94DFA9F93089E593436D2","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import re\n","import jieba.posseg as psg\n","\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")\n","\n","%matplotlib inline\n","\n","# path = '/home/kesci/input/emotion_analysi7147'"]},{"cell_type":"code","execution_count":2,"metadata":{"collapsed":false,"id":"A8EF726DF030469B90396BAE81897FDF","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["reviews = pd.read_csv('./reviews.csv')"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["Building prefix dict from the default dictionary ...\n","Loading model from cache C:\\Users\\Eric\\AppData\\Local\\Temp\\jieba.cache\n","Loading model cost 0.643 seconds.\n","Prefix dict has been built successfully.\n"]},{"data":{"text/plain":["[('，', '7052'),\n"," ('的', 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('比较满意', '6'),\n"," ('具体', '6'),\n"," ('件', '6'),\n"," ('机', '6'),\n"," ('变成', '6'),\n"," ('真差', '6'),\n"," ('老爸', '6'),\n"," ('想想', '6'),\n"," ('天无', '6'),\n"," ('干净', '6'),\n"," ('三百', '6'),\n"," ('每天', '6'),\n"," ('冲', '6'),\n"," ('物品', '6'),\n"," ('超', '6'),\n"," ('两条', '6'),\n"," ('两百', '6'),\n"," ('不敢', '6'),\n"," ('再次', '6'),\n"," ('操', '6'),\n"," ('期间', '6'),\n"," ('因', '6'),\n"," ('带来', '6'),\n"," ('爱', '6'),\n"," ('价位', '6'),\n"," ('透明', '6'),\n"," ('讲解', '6'),\n"," ('打算', '6'),\n"," ('水平', '6'),\n"," ('花钱买', '6'),\n"," ('好几个', '6'),\n"," ('现象', '6'),\n"," ('近', '6'),\n"," ('手机', '6'),\n"," ('帅哥', '6'),\n"," ...]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["reviews = reviews.drop_duplicates(subset=['content','content_type'])\n","content = reviews[\"content\"]\n","# 去除英文、数字、京东、美的、电热水器等词语,pattern\n","strinfo = re.compile('[0-9a-zA-Z]|京东|美的|电热水器|热水器|')\n","content = content.apply(lambda x: strinfo.sub('',x))\n","# 分词\n","worker = lambda s: [(x.word, x.flag) for x in psg.cut(s)] # 自定义简单分词函数\n","seg_word = content.apply(worker)\n","# 删除停用词\n","stop_path = open(\"./stoplist.txt\", 'r',encoding='UTF-8')\n","stop = stop_path.readlines()\n","stop = [x.replace('\\n', '') for x in stop]\n","# 遍历所有词，取出停用词并选出名词，统计词频\n","word_selected=[]\n","for word_set in seg_word:\n","    for w in word_set:\n","        word_selected.append(w[0])\n","word_count = pd.Series(word_selected).value_counts().astype(\"str\")\n","data_wc = list(zip(word_count.index,word_count.values))\n","data_wc"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":["'d:\\\\project\\\\git\\\\my_pda\\\\other_project\\\\good_reviews\\\\basic_wordcloud_diamond.html'"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["import pyecharts.options as opts\n","from pyecharts.charts import WordCloud\n","from pyecharts.globals import SymbolType\n","\n","\n","(\n","    WordCloud()\n","    .add(series_name=\"热点分析\", data_pair=data_wc, word_size_range=[6, 66],shape=SymbolType.DIAMOND)\n","    .set_global_opts(\n","        title_opts=opts.TitleOpts(\n","            title=\"热点分析\", title_textstyle_opts=opts.TextStyleOpts(font_size=23)\n","        ),\n","        tooltip_opts=opts.TooltipOpts(is_show=True),\n","    )\n","    .render(\"basic_wordcloud_diamond.html\")\n","    # .render_notebook()\n",")"]},{"cell_type":"code","execution_count":5,"metadata":{"collapsed":false,"id":"4DC0EEE5C2BD45E88F2BDFB391F57B04","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["(1974, 5)\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>content</th>\n","      <th>creationTime</th>\n","      <th>nickname</th>\n","      <th>referenceName</th>\n","      <th>content_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什...</td>\n","      <td>2017-04-17 13:01:54</td>\n","      <td>鑫***辰</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>安装师傅很给力，热水器也好用，感谢美的。</td>\n","      <td>2017-04-17 10:45:33</td>\n","      <td>切***药</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>还没安装，基本满意</td>\n","      <td>2017-04-17 10:58:33</td>\n","      <td>j***x</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>电热水器收到了，京东自营商品就是好，发货速度快，品质有保障，安装效果好，宝贝非常喜欢，冬天可...</td>\n","      <td>2017-10-18 20:22:33</td>\n","      <td>j***2</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>用了几次才来评价，对产品非常满意，加热快保温时间长，售后服务特别好，主动打电话询问送货情况帮...</td>\n","      <td>2017-04-17 09:19:16</td>\n","      <td>j***6</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                             content         creationTime   \n","0  东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什...  2017-04-17 13:01:54  \\\n","1                               安装师傅很给力，热水器也好用，感谢美的。  2017-04-17 10:45:33   \n","2                                          还没安装，基本满意  2017-04-17 10:58:33   \n","3  电热水器收到了，京东自营商品就是好，发货速度快，品质有保障，安装效果好，宝贝非常喜欢，冬天可...  2017-10-18 20:22:33   \n","4  用了几次才来评价，对产品非常满意，加热快保温时间长，售后服务特别好，主动打电话询问送货情况帮...  2017-04-17 09:19:16   \n","\n","  nickname                            referenceName content_type  \n","0    鑫***辰  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","1    切***药  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","2    j***x  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","3    j***2  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","4    j***6  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["print(reviews.shape)\n","reviews.head()"]},{"cell_type":"code","execution_count":6,"metadata":{"collapsed":false,"id":"C5BDAABAFA5C43158E206E573D3DD74E","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 删除数据记录中所有列值相同的记录\n","reviews = reviews.drop_duplicates(subset=['content','content_type'])\n","content = reviews['content']"]},{"cell_type":"code","execution_count":7,"metadata":{"collapsed":false,"id":"1DB9F88E834C4C5A83D9665E16390FE5","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["(1974, 5)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["reviews.shape"]},{"cell_type":"code","execution_count":8,"metadata":{"collapsed":false,"id":"67CD3A26EDB543D186F52122DC0BBE56","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>content</th>\n","      <th>creationTime</th>\n","      <th>nickname</th>\n","      <th>referenceName</th>\n","      <th>content_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什...</td>\n","      <td>2017-04-17 13:01:54</td>\n","      <td>鑫***辰</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>安装师傅很给力，热水器也好用，感谢美的。</td>\n","      <td>2017-04-17 10:45:33</td>\n","      <td>切***药</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>还没安装，基本满意</td>\n","      <td>2017-04-17 10:58:33</td>\n","      <td>j***x</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>电热水器收到了，京东自营商品就是好，发货速度快，品质有保障，安装效果好，宝贝非常喜欢，冬天可...</td>\n","      <td>2017-10-18 20:22:33</td>\n","      <td>j***2</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>用了几次才来评价，对产品非常满意，加热快保温时间长，售后服务特别好，主动打电话询问送货情况帮...</td>\n","      <td>2017-04-17 09:19:16</td>\n","      <td>j***6</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1995</th>\n","      <td>差评，差的一塌糊涂，千万别买，上当了，</td>\n","      <td>2016-11-25 14:57:52</td>\n","      <td>沫沫19900404</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>neg</td>\n","    </tr>\n","    <tr>\n","      <th>1996</th>\n","      <td>热水器还没有安装，就搞一肚子气，安装人员今天推明天，明天推后天，售后安装服务太差，给差评，目...</td>\n","      <td>2016-11-25 13:39:28</td>\n","      <td>j***l</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>neg</td>\n","    </tr>\n","    <tr>\n","      <th>1997</th>\n","      <td>好不容易网购一下还漏电</td>\n","      <td>2016-11-25 13:38:49</td>\n","      <td>K***T</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>neg</td>\n","    </tr>\n","    <tr>\n","      <th>1998</th>\n","      <td>东西送的挺快，后期报装2天还没人联系我，售后太差</td>\n","      <td>2016-11-25 10:19:20</td>\n","      <td>j***p</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>neg</td>\n","    </tr>\n","    <tr>\n","      <th>1999</th>\n","      <td>买了两个，送到一个，还有一个至今未送到。</td>\n","      <td>2016-11-25 10:17:45</td>\n","      <td>s***8</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>neg</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1974 rows × 5 columns</p>\n","</div>"],"text/plain":["                                                content         creationTime   \n","0     东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什...  2017-04-17 13:01:54  \\\n","1                                  安装师傅很给力，热水器也好用，感谢美的。  2017-04-17 10:45:33   \n","2                                             还没安装，基本满意  2017-04-17 10:58:33   \n","3     电热水器收到了，京东自营商品就是好，发货速度快，品质有保障，安装效果好，宝贝非常喜欢，冬天可...  2017-10-18 20:22:33   \n","4     用了几次才来评价，对产品非常满意，加热快保温时间长，售后服务特别好，主动打电话询问送货情况帮...  2017-04-17 09:19:16   \n","...                                                 ...                  ...   \n","1995                                差评，差的一塌糊涂，千万别买，上当了，  2016-11-25 14:57:52   \n","1996  热水器还没有安装，就搞一肚子气，安装人员今天推明天，明天推后天，售后安装服务太差，给差评，目...  2016-11-25 13:39:28   \n","1997                                        好不容易网购一下还漏电  2016-11-25 13:38:49   \n","1998                           东西送的挺快，后期报装2天还没人联系我，售后太差  2016-11-25 10:19:20   \n","1999                               买了两个，送到一个，还有一个至今未送到。  2016-11-25 10:17:45   \n","\n","        nickname                            referenceName content_type  \n","0          鑫***辰  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","1          切***药  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","2          j***x  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","3          j***2  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","4          j***6  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","...          ...                                      ...          ...  \n","1995  沫沫19900404  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          neg  \n","1996       j***l  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          neg  \n","1997       K***T  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          neg  \n","1998       j***p  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          neg  \n","1999       s***8  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          neg  \n","\n","[1974 rows x 5 columns]"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["reviews"]},{"cell_type":"markdown","metadata":{"id":"D5E1CCC17A124AEF8648F97549E8FE3F","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["### 数据清洗  \n","\n","通过人工观察数据发现，评论中夹杂着许多数字与字母，对于本案例的挖掘目标而言，这类数据本身并没有实质性帮助。另外，由于该评论文本数据主要是围绕京东商城中美的电热水器进行评价的，其中“京东”“京东商城”“美的”“热水器”“电热水器”等词出现的频数很大，但是对分析目标并没有什么作用，因此可以在分词之前将这些词去除，对数据进行清洗"]},{"cell_type":"code","execution_count":9,"metadata":{"collapsed":false,"id":"091545468C01468599895BC0666485E3","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 去除英文、数字、京东、美的、电热水器等词语,pattern\n","strinfo = re.compile('[0-9a-zA-Z]|京东|美的|电热水器|热水器|')\n","content = content.apply(lambda x: strinfo.sub('',x))"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/plain":["'我什么都买了，就是'"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["strinfo.sub('',\"我什么都买了，就是meiyou美的\")"]},{"cell_type":"markdown","metadata":{"id":"807802B48AAC4E818C24B89D4B26DC39","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["### 分词、词性标注、去除停用词  \n","\n","词是文本信息处理的基础环节，是将一个单词序列切分成单个单词的过程。准确地分词可以极大地提高计算机对文本信息的识别和理解能力。相反，不准确的分词将会产生大量的噪声，严重干扰计算机的识别理解能力，并对这些信息的后续处理工作产生较大的影响。中文分词的任务就是把中文的序列切分成有意义的词，即添加合适的词串使得所形成的词串反映句子的本意，中文分词的关键问题为切分歧义的消解和未登录词的识别。  \n","\n","未登录词是指词典中没有登录过的人名、地名、机构名、译名及新词语等。当采用匹配的办法来切分词语时，由于词典中没有登录这些词，会引起自动切分词语的困难。  \n","\n","分词最常用的工作包是jieba分词包，jieba分词是Python写成的一个分词开源库，专门用于中文分词，其有3条基本原理，即实现所采用技术。  \n","1. 基于Trie树结构实现高效的词图扫描，生成句子中汉字所有可能成词情况所构成的有向无环图（DAG）。  \n","2. 采用动态规划查找最大概率路径，找出基于词频的最大切分组合。  \n","3. 对于未登录词，采用HMM模型，使用了Viterbi算法，将中文词汇按照BEMS 4个状态来标记。"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/plain":["['值得',\n"," '美的',\n"," '个人感觉',\n"," '东西',\n"," '这么久',\n"," '好评',\n"," '信赖',\n"," '收到',\n"," '不错',\n"," '品牌',\n"," '拥有',\n"," '整体',\n"," '来看',\n"," '问题',\n"," '出现',\n"," '什么',\n"," '没有']"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["import jieba.analyse\n","jieba.analyse.extract_tags(\"东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什么问题，值得拥有！\")"]},{"cell_type":"code","execution_count":12,"metadata":{"collapsed":false,"id":"517383AC34504E6D86A327EC238F1078","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 分词\n","worker = lambda s: [(x.word, x.flag) for x in psg.cut(s)] # 自定义简单分词函数\n","seg_word = content.apply(worker)"]},{"cell_type":"code","execution_count":13,"metadata":{"collapsed":false,"id":"E67CC614CA7049AA8B95DC4601A07BFD","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["0    [(东西, ns), (收到, v), (这么久, r), (，, x), (都, d), ...\n","1    [(安装, v), (师傅, nr), (很, d), (给, p), (力, n), (，...\n","2    [(还, d), (没, v), (安装, v), (，, x), (基本, n), (满意...\n","3    [(收到, v), (了, ul), (，, x), (自营, vn), (商品, n), ...\n","4    [(用, p), (了, ul), (几次, m), (才, d), (来, v), (评价...\n","Name: content, dtype: object"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["seg_word.head()"]},{"cell_type":"code","execution_count":14,"metadata":{"collapsed":false,"id":"AFE9036ED9124F7E8DDD4B8843F51AE8","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 将词语转为数据框形式，一列是词，一列是词语所在的句子ID，最后一列是词语在该句子的位置\n","n_word = seg_word.apply(lambda x: len(x))  # 每一评论中词的个数\n","\n","n_content = [[x+1]*y for x,y in zip(list(seg_word.index), list(n_word))]\n","\n","# 将嵌套的列表展开，作为词所在评论的id\n","index_content = sum(n_content, [])\n","\n","seg_word = sum(seg_word, [])\n","# 词\n","word = [x[0] for x in seg_word]\n","# 词性\n","nature = [x[1] for x in seg_word]\n","\n","content_type = [[x]*y for x,y in zip(list(reviews['content_type']), list(n_word))]\n","# 评论类型\n","content_type = sum(content_type, [])\n","\n","result = pd.DataFrame({\"index_content\":index_content, \n","                       \"word\":word,\n","                       \"nature\":nature,\n","                       \"content_type\":content_type})"]},{"cell_type":"code","execution_count":15,"metadata":{"collapsed":false,"id":"45EE0D70974E4BAE91C874201457C664","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>index_content</th>\n","      <th>word</th>\n","      <th>nature</th>\n","      <th>content_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>东西</td>\n","      <td>ns</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>收到</td>\n","      <td>v</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>这么久</td>\n","      <td>r</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>，</td>\n","      <td>x</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>都</td>\n","      <td>d</td>\n","      <td>pos</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   index_content word nature content_type\n","0              1   东西     ns          pos\n","1              1   收到      v          pos\n","2              1  这么久      r          pos\n","3              1    ，      x          pos\n","4              1    都      d          pos"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["result.head()"]},{"cell_type":"code","execution_count":16,"metadata":{"collapsed":false,"id":"8067AA51DDB7422785B80F11E1DD7506","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 删除标点符号\n","result = result[result['nature'] != 'x']  # x表示标点符号\n","\n","# 删除停用词\n","stop_path = open(\"./stoplist.txt\", 'r',encoding='UTF-8')\n","stop = stop_path.readlines()\n","stop = [x.replace('\\n', '') for x in stop]\n","word = list(set(word) - set(stop))\n","result = result[result['word'].isin(word)]"]},{"cell_type":"code","execution_count":17,"metadata":{"collapsed":false,"id":"B6E4BCF72A9044F98ECF8288204BEFA9","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    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     <th>5</th>\n","      <td>1</td>\n","      <td>忘</td>\n","      <td>v</td>\n","      <td>pos</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>好评</td>\n","      <td>v</td>\n","      <td>pos</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   index_content word nature content_type  index_word\n","0              1   东西     ns          pos           0\n","1              1   收到      v          pos           1\n","2              1  这么久      r          pos           2\n","5              1    忘      v          pos           3\n","8              1   好评      v          pos           4"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["# 构造各词在对应评论的位置列\n","n_word = list(result.groupby(by = ['index_content'])['index_content'].count())\n","index_word = [list(np.arange(0, y)) for y in n_word]\n","# 词语在该评论的位置\n","index_word = sum(index_word, [])\n","# 合并评论id\n","result['index_word'] = index_word\n","\n","result.head()"]},{"cell_type":"markdown","metadata":{"id":"20D4771809DF40BF97EB2A9E6D76F5F7","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["### 提取含名词的评论  \n","\n","由于本案例的目标是对产品特征的优缺点进行分析，类似“不错，很好的产品”“很不错，继续支持”等评论虽然表达了对产品的情感倾向，但是实际上无法根据这些评论提取出哪些产品特征是用户满意的。评论中只有出现明确的名词，如机构团体及其他专有名词时，才有意义，因此需要对分词后的词语进行词性标注。之后再根据词性将含有名词类的评论提取出来。"]},{"cell_type":"code","execution_count":19,"metadata":{"collapsed":false,"id":"3F344D8696D641EFAF288D97F019A2E9","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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'x'),\n"," ('质量', 'n'),\n"," ('非常', 'd'),\n"," ('好', 'a'),\n"," ('，', 'x'),\n"," ('与', 'p'),\n"," ('卖家', 'n'),\n"," ('描述', 'v'),\n"," ('的', 'uj'),\n"," ('完全一致', 'i'),\n"," ('，', 'x'),\n"," ('非常', 'd'),\n"," ('满意', 'v'),\n"," (',', 'x'),\n"," ('真的', 'd'),\n"," ('很', 'zg'),\n"," ('喜欢', 'v'),\n"," ('，', 'x'),\n"," ('完全', 'ad'),\n"," ('超出', 'v'),\n"," ('期望值', 'n'),\n"," ('，', 'x'),\n"," (' ', 'x'),\n"," ('发货', 'n'),\n"," ('速度', 'n'),\n"," (' ', 'x'),\n"," ('非常', 'd'),\n"," ('快', 'a'),\n"," ('，', 'x'),\n"," ('包装', 'v'),\n"," ('非常', 'd'),\n"," ('仔细', 'ad'),\n"," ('、', 'x'),\n"," ('严实', 'ad'),\n"," ('，', 'x'),\n"," (' ', 'x'),\n"," ('物流', 'n'),\n"," ('公司', 'n'),\n"," (' ', 'x'),\n"," ...]"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["seg_word"]},{"cell_type":"markdown","metadata":{"id":"9160F32BE7FC43518DC2AC3BAD654EA8","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["### 绘制词云  \n","\n","绘制词云查看分词效果，词云会将文本中出现频率较高的“关键词”予以视觉上的突出。首先需要对词语进行词频统计，将词频按照降序排序，选择前100个词，使用wordcloud模块中的WordCloud绘制词云，查看分词效果"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"ename":"TypeError","evalue":"can only concatenate list (not \"tuple\") to list","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[1;32md:\\project\\git\\my_pda\\other_project\\good_reviews\\电商产品评论数据情感分析.ipynb Cell 26\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X34sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m seg_word \u001b[39m=\u001b[39m \u001b[39msum\u001b[39;49m(seg_word, [])\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X34sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m result_with_stop\u001b[39m=\u001b[39m[]\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X34sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mfor\u001b[39;00m w \u001b[39min\u001b[39;00m seg_word:\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X34sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     \u001b[39m# if (str(w[1]).__contains__('n')) & (str(w[0]) not in stop):\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X34sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     \u001b[39m# if (str(w[1]).__contains__('n')) :\u001b[39;00m\n","\u001b[1;31mTypeError\u001b[0m: can only concatenate list (not \"tuple\") to list"]}],"source":["seg_word = sum(seg_word, [])\n","result_with_stop=[]\n","for w in seg_word:\n","    # if (str(w[1]).__contains__('n')) & (str(w[0]) not in stop):\n","    # if (str(w[1]).__contains__('n')) :\n","    result_with_stop.append(w[0])\n","result_with_stop = pd.DataFrame(result_with_stop,columns=[\"word\"])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>word</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>东西</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>美的</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>品牌</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>信赖</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>东西</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>13043</th>\n","      <td>网购</td>\n","    </tr>\n","    <tr>\n","      <th>13044</th>\n","      <td>漏电</td>\n","    </tr>\n","    <tr>\n","      <th>13045</th>\n","      <td>东西</td>\n","    </tr>\n","    <tr>\n","      <th>13046</th>\n","      <td>联系</td>\n","    </tr>\n","    <tr>\n","      <th>13047</th>\n","      <td>售后</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>13048 rows × 1 columns</p>\n","</div>"],"text/plain":["      word\n","0       东西\n","1       美的\n","2       品牌\n","3       信赖\n","4       东西\n","...    ...\n","13043   网购\n","13044   漏电\n","13045   东西\n","13046   联系\n","13047   售后\n","\n","[13048 rows x 1 columns]"]},"execution_count":80,"metadata":{},"output_type":"execute_result"}],"source":["result_with_stop"]},{"cell_type":"code","execution_count":24,"metadata":{"collapsed":false,"id":"CF367D33FEF34A23848ED591D0C2D97F","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"ename":"NameError","evalue":"name 'result_with_stop' is not defined","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[1;32md:\\project\\git\\my_pda\\other_project\\good_reviews\\电商产品评论数据情感分析.ipynb Cell 28\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X36sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X36sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mwordcloud\u001b[39;00m \u001b[39mimport\u001b[39;00m WordCloud\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X36sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m frequencies \u001b[39m=\u001b[39m result_with_stop\u001b[39m.\u001b[39mgroupby(\u001b[39m'\u001b[39m\u001b[39mword\u001b[39m\u001b[39m'\u001b[39m)[\u001b[39m'\u001b[39m\u001b[39mword\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mcount()\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X36sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m frequencies \u001b[39m=\u001b[39m frequencies\u001b[39m.\u001b[39msort_values(ascending \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X36sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m backgroud_Image\u001b[39m=\u001b[39mplt\u001b[39m.\u001b[39mimread(\u001b[39m'\u001b[39m\u001b[39m./pl.jpg\u001b[39m\u001b[39m'\u001b[39m)\n","\u001b[1;31mNameError\u001b[0m: name 'result_with_stop' is not defined"]}],"source":["import matplotlib.pyplot as plt\n","from wordcloud import WordCloud\n","\n","frequencies = result_with_stop.groupby('word')['word'].count()\n","frequencies = frequencies.sort_values(ascending = False)\n","backgroud_Image=plt.imread('./pl.jpg')\n","\n","# 自己上传中文字体到kesci\n","# font_path = '/home/kesci/work/data/fonts/MSYHL.TTC'\n","wordcloud = WordCloud(font_path='simsun.ttc', # 设置字体，不设置就会出现乱码\n","                      max_words=100,\n","                      background_color='white',\n","                      mask=backgroud_Image)# 词云形状\n","\n","my_wordcloud = wordcloud.fit_words(frequencies)\n","plt.imshow(my_wordcloud)\n","plt.axis('off') \n","plt.show()"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"ename":"NameError","evalue":"name 'frequencies' is not defined","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[1;32md:\\project\\git\\my_pda\\other_project\\good_reviews\\电商产品评论数据情感分析.ipynb Cell 29\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X40sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m frequencies \u001b[39m=\u001b[39m frequencies\u001b[39m.\u001b[39mastype(\u001b[39m\"\u001b[39m\u001b[39mstr\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#X40sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m data_wc \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(\u001b[39mzip\u001b[39m(frequencies\u001b[39m.\u001b[39mindex,frequencies\u001b[39m.\u001b[39mvalues))\n","\u001b[1;31mNameError\u001b[0m: name 'frequencies' is not defined"]}],"source":["frequencies = frequencies.astype(\"str\")\n","data_wc = list(zip(frequencies.index,frequencies.values))"]},{"cell_type":"markdown","metadata":{"id":"9A5FEE2739FA42008F9D43459D8F2E23","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["由图可以看出，对评论数据进行预处理后，分词效果较为符合预期。其中“安装”“师傅”“售后”“物流”“服务”等词出现频率较高，因此可以初步判断用户对产品的这几个方面比较重视"]},{"cell_type":"code","execution_count":26,"metadata":{"collapsed":false,"id":"A6E8F57F1EF5410FAD171E469E74F761","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 将结果保存\n","result.to_csv(\"./word.csv\", index = False, encoding = 'utf-8')"]},{"cell_type":"markdown","metadata":{"id":"628E687861294D7489906047391A55E2","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["## 词典匹配  \n","\n","### 评论数据情感倾向分析  \n","\n","匹配情感词情感倾向也称为情感极性。在某商品评论中，可以理解为用户对该商品表达自身观点所持的态度是支持、反对还是中立，即通常所指的正面情感、负面情感、中性情感。由于本案例主要是对产品的优缺点进行分析，因此只要确定用户评论信息中的情感倾向方向分析即可，不需要分析每一评论的情感程度。  \n","\n","对评论情感倾向进行分析首先要对情感词进行匹配，主要采用词典匹配的方法，本案例使用的情感词表是2007年10月22日知网发布的“情感分析用词语集（beta版）”，主要使用“中文正面评价”词表、“中文负面评价”“中文正面情感”“中文负面情感”词表等。将“中文正面评价”“中文正面情感”两个词表合并，并给每个词语赋予初始权重1，作为本案例的正面评论情感词表。将“中文负面评价”“中文负面情感”两个词表合并，并给每个词语赋予初始权重-1，作为本案例的负面评论情感词表。  \n","\n","一般基于词表的情感分析方法，分析的效果往往与情感词表内的词语有较强的相关性，如果情感词表内的词语足够全面，并且词语符合该案例场景下所表达的情感，那么情感分析的效果会更好。针对本案例场景，需要在知网提供的词表基础上进行优化，例如“好评”“超值”“差评”“五分”等词只有在网络购物评论上出现，就可以根据词语的情感倾向添加至对应的情感词表内。将“满意”“好评”“很快”“还好”“还行”“超值”“给力”“支持”“超好”“感谢”“太棒了”“厉害”“挺舒服”“辛苦”“完美”“喜欢”“值得”“省心”等词添加进正面情感词表。将“差评”“贵”“高”“漏水”等词加入负面情感词表。读入正负面评论情感词表，正面词语赋予初始权重1，负面词语赋予初始权重-1。"]},{"cell_type":"code","execution_count":30,"metadata":{"collapsed":false,"id":"DCD89D9B46074D2B9BEDA3744797C40C","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>word</th>\n","      <th>weight</th>\n","      <th>index_content</th>\n","      <th>nature</th>\n","      <th>content_type</th>\n","      <th>index_word</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>东西</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>ns</td>\n","      <td>pos</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>收到</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>v</td>\n","      <td>pos</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>这么久</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>r</td>\n","      <td>pos</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>忘</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>v</td>\n","      <td>pos</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>好评</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","      <td>v</td>\n","      <td>pos</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  word  weight  index_content nature content_type  index_word\n","0   东西     NaN              1     ns          pos           0\n","1   收到     NaN              1      v          pos           1\n","2  这么久     NaN              1      r          pos           2\n","3    忘     NaN              1      v          pos           3\n","4   好评     1.0              1      v          pos           4"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["word = pd.read_csv(\"./word.csv\")\n","\n","# 读入正面、负面情感评价词\n","pos_comment = pd.read_csv(\"./正面评价词语（中文）.txt\", header=None,sep=\"/n\", \n","                          encoding = 'utf-8', engine='python')\n","neg_comment = pd.read_csv(\"./负面评价词语（中文）.txt\", header=None,sep=\"/n\", \n","                          encoding = 'utf-8', engine='python')\n","pos_emotion = pd.read_csv(\"./正面情感词语（中文）.txt\", header=None,sep=\"/n\", \n","                          encoding = 'utf-8', engine='python')\n","neg_emotion = pd.read_csv(\"./负面情感词语（中文）.txt\", header=None,sep=\"/n\", \n","                          encoding = 'utf-8', engine='python') \n","\n","# 合并情感词与评价词\n","positive = set(pos_comment.iloc[:,0])|set(pos_emotion.iloc[:,0])\n","negative = set(neg_comment.iloc[:,0])|set(neg_emotion.iloc[:,0])\n","\n","# 正负面情感词表中相同的词语\n","intersection = positive&negative\n","\n","positive = list(positive - intersection)\n","negative = list(negative - intersection)\n","\n","positive = pd.DataFrame({\"word\":positive,\n","                         \"weight\":[1]*len(positive)})\n","negative = pd.DataFrame({\"word\":negative,\n","                         \"weight\":[-1]*len(negative)}) \n","\n","posneg = positive._append(negative)\n","# posneg = pd.concat([positive,negative],axis=1)\n","\n","\n","\n","# 将分词结果与正负面情感词表合并，定位情感词\n","data_posneg = posneg.merge(word, left_on = 'word', right_on = 'word', \n","                           how = 'right')\n","data_posneg = data_posneg.sort_values(by = ['index_content','index_word'])\n","\n","data_posneg.head()"]},{"cell_type":"markdown","metadata":{"id":"5EF6EB9920F0407A9752B36DE21F7629","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["### 修正情感倾向  \n","\n","情感倾向修正主要根据情感词前面两个位置的词语是否存在否定词而去判断情感值的正确与否，由于汉语中存在多重否定现象，即当否定词出现奇数次时，表示否定意思；当否定词出现偶数次时，表示肯定意思。按照汉语习惯，搜索每个情感词前两个词语，若出现奇数否定词，则调整为相反的情感极性。  \n","\n","本案例使用的否定词表共有19个否定词，分别为：不、没、无、非、莫、弗、毋、未、否、别、無、休、不是、不能、不可、没有、不用、不要、从没、不太。  \n","\n","读入否定词表，对情感值的方向进行修正。计算每条评论的情感得分，将评论分为正面评论和负面评论，并计算情感分析的准确率。"]},{"cell_type":"code","execution_count":32,"metadata":{"collapsed":false,"id":"E71016149DA741CB8D1925CF14620DBE","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 载入否定词表\n","notdict = pd.read_csv(\"./not.csv\")\n","\n","# 构造新列，作为经过否定词修正后的情感值\n","data_posneg['amend_weight'] = data_posneg['weight']\n","data_posneg['id'] = np.arange(0, len(data_posneg))\n","\n","# 只保留有情感值的词语\n","only_inclination = data_posneg.dropna().reset_index(drop=True)\n","\n","index = only_inclination['id']\n","\n","\n","for i in np.arange(0, len(only_inclination)):\n","    # 提取第i个情感词所在的评论\n","    review = data_posneg[data_posneg['index_content'] == only_inclination['index_content'][i]]\n","    review.index = np.arange(0, len(review))\n","    # 第i个情感值在该文档的位置\n","    affective = only_inclination['index_word'][i]\n","    if affective == 1:\n","        ne = sum([i in notdict['term'] for i in review['word'][affective - 1]])%2\n","        if ne == 1:\n","            data_posneg['amend_weight'][index[i]] = -data_posneg['weight'][index[i]]          \n","    elif affective > 1:\n","        ne = sum([i in notdict['term'] for i in review['word'][[affective - 1, \n","                  affective - 2]]])%2\n","        if ne == 1:\n","            data_posneg['amend_weight'][index[i]] = -data_posneg['weight'][index[i]]\n","            \n","\n","            \n","# 更新只保留情感值的数据\n","only_inclination = only_inclination.dropna()\n","\n","# 计算每条评论的情感值\n","emotional_value = only_inclination.groupby(['index_content'],\n","                                           as_index=False)['amend_weight'].sum()\n","\n","# 去除情感值为0的评论\n","emotional_value = emotional_value[emotional_value['amend_weight'] != 0]"]},{"cell_type":"markdown","metadata":{"id":"C7A82636137D46E088C2329CF92867F0","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["### 查看情感分析效果"]},{"cell_type":"code","execution_count":33,"metadata":{"collapsed":false,"id":"CE3A6B741DBD45108B5DDF8A6138DF22","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>index_content</th>\n","      <th>amend_weight</th>\n","      <th>a_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>5.0</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1.0</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4</td>\n","      <td>4.0</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>5</td>\n","      <td>3.0</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>6</td>\n","      <td>2.0</td>\n","      <td>pos</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   index_content  amend_weight a_type\n","0              1           5.0    pos\n","1              2           1.0    pos\n","2              4           4.0    pos\n","3              5           3.0    pos\n","4              6           2.0    pos"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["# 给情感值大于0的赋予评论类型（content_type）为pos,小于0的为neg\n","emotional_value['a_type'] = ''\n","emotional_value['a_type'][emotional_value['amend_weight'] > 0] = 'pos'\n","emotional_value['a_type'][emotional_value['amend_weight'] < 0] = 'neg'\n","\n","emotional_value.head()"]},{"cell_type":"code","execution_count":34,"metadata":{"collapsed":false,"id":"20DF335F909B45A39704FF8B2EAF564C","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>index_content</th>\n","      <th>amend_weight</th>\n","      <th>a_type</th>\n","      <th>word</th>\n","      <th>nature</th>\n","      <th>content_type</th>\n","      <th>index_word</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>5.0</td>\n","      <td>pos</td>\n","      <td>东西</td>\n","      <td>ns</td>\n","      <td>pos</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>5.0</td>\n","      <td>pos</td>\n","      <td>收到</td>\n","      <td>v</td>\n","      <td>pos</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>5.0</td>\n","      <td>pos</td>\n","      <td>这么久</td>\n","      <td>r</td>\n","      <td>pos</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>5.0</td>\n","      <td>pos</td>\n","      <td>忘</td>\n","      <td>v</td>\n","      <td>pos</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>5.0</td>\n","      <td>pos</td>\n","      <td>好评</td>\n","      <td>v</td>\n","      <td>pos</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   index_content  amend_weight a_type word nature content_type  index_word\n","0              1           5.0    pos   东西     ns          pos           0\n","1              1           5.0    pos   收到      v          pos           1\n","2              1           5.0    pos  这么久      r          pos           2\n","3              1           5.0    pos    忘      v          pos           3\n","4              1           5.0    pos   好评      v          pos           4"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["# 查看情感分析结果\n","result = emotional_value.merge(word, \n","                               left_on = 'index_content', \n","                               right_on = 'index_content',\n","                               how = 'left')\n","result.head()"]},{"cell_type":"code","execution_count":35,"metadata":{"collapsed":false,"id":"E15356CF66EF47E08549E223BAF1CC38","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>index_content</th>\n","      <th>content_type</th>\n","      <th>a_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>pos</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>2</td>\n","      <td>pos</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>4</td>\n","      <td>pos</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>5</td>\n","      <td>pos</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>61</th>\n","      <td>6</td>\n","      <td>pos</td>\n","      <td>pos</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    index_content content_type a_type\n","0               1          pos    pos\n","14              2          pos    pos\n","18              4          pos    pos\n","38              5          pos    pos\n","61              6          pos    pos"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["result = result[['index_content','content_type', 'a_type']].drop_duplicates()\n","result.head()"]},{"cell_type":"markdown","metadata":{"id":"976679C15AAF4943AEF3581CB2E7BF6C","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["假定用户在评论时不存在“选了好评的标签，而写了差评内容”的情况，比较原评论的评论类型与情感分析得出的评论类型，绘制情感倾向分析混淆矩阵，查看词表的情感分析的准确率。"]},{"cell_type":"code","execution_count":36,"metadata":{"collapsed":false,"id":"37532DBBB83E44EF83C946A24C3D182B","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>a_type</th>\n","      <th>neg</th>\n","      <th>pos</th>\n","      <th>All</th>\n","    </tr>\n","    <tr>\n","      <th>content_type</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>neg</th>\n","      <td>487</td>\n","      <td>175</td>\n","      <td>662</td>\n","    </tr>\n","    <tr>\n","      <th>pos</th>\n","      <td>31</td>\n","      <td>701</td>\n","      <td>732</td>\n","    </tr>\n","    <tr>\n","      <th>All</th>\n","      <td>518</td>\n","      <td>876</td>\n","      <td>1394</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["a_type        neg  pos   All\n","content_type                \n","neg           487  175   662\n","pos            31  701   732\n","All           518  876  1394"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["# 交叉表:统计分组频率的特殊透视表\n","confusion_matrix = pd.crosstab(result['content_type'], result['a_type'], \n","                               margins=True)\n","confusion_matrix.head()"]},{"cell_type":"code","execution_count":37,"metadata":{"collapsed":false,"id":"F77BFC4DB2994DA49579970D9963ACA6","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["0.8522238163558106"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["(confusion_matrix.iat[0,0] + confusion_matrix.iat[1,1])/confusion_matrix.iat[2,2]"]},{"cell_type":"code","execution_count":38,"metadata":{"collapsed":false,"id":"0BC21841DD474CE5B20FF1A338D3BB96","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 提取正负面评论信息\n","ind_pos = list(emotional_value[emotional_value['a_type'] == 'pos']['index_content'])\n","ind_neg = list(emotional_value[emotional_value['a_type'] == 'neg']['index_content'])\n","posdata = word[[i in ind_pos for i in word['index_content']]]\n","negdata = word[[i in ind_neg for i in word['index_content']]]"]},{"cell_type":"code","execution_count":41,"metadata":{"collapsed":false,"id":"D4EC34AD42364E589966F9E7E53EF2D7","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure 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ta6iKwDAtqsuC5AomPreOrqkkMvlfaM/OL6UQmYYQgu7OUTZf04aul3osPN6ZXBcJpFN5ujtHGR9LYttzE9YkkpOTw+Qtc8EPJl7IMphJYC9C4MSNUY5MPkfcGMOleOlIHeXwxDOM5HsWvMeSJs+O/JiDE3u5o+YBbq2+D01xhJ2uuLiv7gNMGON8vfvTTBrjSClJGBN8o/tf6UqfY2VwPR9s+hRhvYysYXB2ZIxUPk9tKEgyn2d5VQVZw8CwbF7u7GE8neH08BjnRscXnPRvRomX0saUBoY9948pTWwpp2wWWX4y+B0+3f4X9GTa54ytEIIT8XY60n2oQkURgjtrr2ckFwOg2V+HYZuM5Mdp8zcgkaTNDLFCnP7sMAXbYDg3Tt4qlLQbT+VY1lBJ0OdG1xQKpoVhWrT3jxHye9A0lc7BGJlc6X2/aPil3M5MY899m3C5NDRNQQjB1p1L8PlmYg2iUT/bdi7F5dK46x2bCYW91NRF0LS5LlJLSv7y8LO4VY3/uuEmlobK5+yhOxMxfveVH3Bf8xo+vuIaIu75t0RSSjyqn3J3HQdjj9HiX0tQL8eteKfCyOfCkhYvjD7BS2NPcX/d+9levrskJH3apvDepk/ypc6/57Mdf80dNe/kpdEnOZc6yZrQJh5q+tWiW9er60R8XjTFmXTGFJVBc1kERQiua2vmyTPnWV5VTiJ3MTqBNxacB4LR/DDf7PkcljTJ2dkF7Tl5K8vp5FEsafH17k/za0v+kPIL7C/1viraAg30ZobYP34MRSjkrQIbIsvZXrYOgIDmw6VonE12owmVW6t3oAqF04lO9sWOcV3FJtzqjLZYXxGid2SS6mgQv8fF4XMDVEb8hHweLNsmky/QUhOlpix41bD6iwghBH6/u+RY8AI7h+7S0F3OEIUjzn7Y49EZy2UIKW5cilpiRLSR9KfjeNT5h1VTFEayKUzbRrtEElhn+iimbbIqvJPhXBcpc5KgXkarf92ca03b5IXRxzg08TLvb/o1lgfXzutJEUJQ7a7jg82/wRc7/44vdf4DNhZrw1t4X9Mn8aszH7stJZOZHEGPi+5YnltWLOH82DiN0Qit5VF8Lh1dVQl5HI+Ls/hnL/pOl4tydxXvrP8QE8YYP+j/OvfVvQ9dcc+5rj11ilOJo2hCZ0vZtQT18JxrylzOsZWhVlaG5tqspJQ0eKuRSMpc4ZJJvzG6kg2RFSXXCyHwunW2rWzE73EhBNy1YyW6prJlRcOUt0eiCifn6RcZv7RC5M3gaGyAR3pO8r4lm9hc0TAlEASaUGgMRGjwh+ddeRShoAqFndXNBPTSyXBh3EibfyNDuU504abS3UBIq6Ag88SNMaKu6uJ9eSvHMyOPMFkY52Otv0uZq/Li4dzYTBrjmNJ0UvlRSBpxBrI9tPlXouIIxrF0hqDbhW1LBhNJJrM58qZJ9/gEUa+Hlzt6UIUglS/QEAlzcmgEw5ob8zH9VAsL0y61mdhY87q1p6EKFb8WKObEpMwkjb6KOeM2khvAxqLFt5Sbqu7BdREj64LjImFsPElZ1I+qlhqdU6kcgYBnblKkEASnNFfblsRiaSrKA2iq8guteVyIXwohslAexBvJjxBCsLtuCT2pCT714nd5Z+s6fn/9DbgUDeUS7QjmjwKXUrJ3qIuz8VE+sGwzblXDrXpp8q0ibU7i00K4FA9BUU7OSpfcmzAnqfc2c2v1/Whi4QQ+KSVpK8VzIz/mhdHH8ag+Hmj4MABPDf+Az3b8NatDG7m+8naafG1UBfxUBfwArK6tImuYWLaNV9dwqRq7l7fid81M1pbyKEsr54/iNWSBH/Z/Yy5niZQMZnsvOmYALsVNg6+V4/HXaPC2lLyjJS3OTbmI14W3zolaXSxM0+Iv/+ZR7rlzAzdeX6p1PPHUCWpqwuzcvnRBrcK2bf75M09z281ruHbnsnntZqZp0T8wQVtrJcovEB3Bz6UQcSa/RLLQylcKw5pkPLePav/tqMJdbGMyf5icOUCNfw9inpR2p1xCDkWUrkKqUHj/0s2ciA3zjfOHuL95Dauj1QgcFfbAaC+xfAb1gi1FfzqOadu8PtZPzppZlWP5DP90/CUmClniRo7fXL0LVZm2jQQo2Dl04WbSGMavlqrqFa5qKlwL57BMl3w4lzrJjwe/zWC2lzXhzdxZ+y6q3fUALAus5vGh73E8fpAT8UM0+drYGN3B8uBayl2VuFR9DplPQC3VpCqnBM6c5yPRhYs9NQ9Q5qosOWdj85Wuf6QjfWbee2djVWgDPx74FjdW7sGnzUSOThrj9GW78Kl+1oTnEjYtFsLJzKO1pfKC44JNG5v50//n+zQ2lNHUuFC6g8C2JbW1EU6dHmBkNElH5yhut8bSJY7m2N0zxg8eeZ2/+PMHaW6qWKCdnz/8XAoRgJw1xGTuMLoSglmTNWv2M5Z5ifrgO1GEs1IaVpycOUTeHMWnNxSvjeePYNhxQCClzUTutSlyY+dDNOw4/cnvsqLsvxFwtTLbSOhSVH5jzS4yVoGGQASmzipCoAhB2ihQ5w/jmpWnkjTyDimQy02ZeybmIOr28be77gccu4ktJSPZTkZy3UX3paboFOw86yM3lozDxSaNLW2Gcn08M/IIRycPUO6u4gPNv87a8NYSraXG08AHmz/F6eQxnhn+EV2Zc7SnT+NVfVS562jxL3O0E08dYT2KW/GgK7qTss9cjpNpSGxsaaEIlaAeJqCXGkYdHpC5n6Cc9d/pd2z2LcWUJqcSR9gc3VXMrD6bPEHGTLE2vIUKd/WcthYPgaIoRc4Wx8XunKmtDdPQUIZtSwzD4vjJPlatrMPj1i9owfnt16yuZw0wNBwnEHCz45o2AM6eG+KGa5dTX1f2Jvr59sPPpRBxojVrqfHXXnBGcm7i7/Fp9VR6b+BSnoGM0UO1//ap6yRhz/pZ2R6SnvhXibg341Ln/uhCCFqDZfzdzvtRFYVz8TEGM0kaAmG2VDSwpaJhzuTy6y5UobAsVMGWyoY5bc5GSJYTdVWTNuNkrDiWtPBJeck0/unozOFcP3vHnuLw5H5cipvba97JjvLdJcbT2e+iCZ01oU0sC6zmVOIIL449QVf6HN2Z83RnzgOgCx2P6sOvBfCrQQJ6mM2RHWyM7pi3L5a0MGwDTWhob3CbMQ2f6mdteDMvjj3BmvBmPKoXU5q8PvEKAoVrym5AYeHEwsVACGf8hobjqKrCode78U3RWl63cxm9fTHOnR/m4KFOTNNm6+YWpIRCwcS0bCzbJpcz2PdqO22tjkajKo59JJnMcfL0AL/3W7eX2Fx+EfBzKURg/hU4awwwmnmWdZV/AQuskFJaTOaPYssCaaMDw44zlP4JOWuYltBHEELFsnOkjQ7cahU1gbtK6P9mw0bSk5rku51HMWwbXS11qb4ZpM04w7lOhFCwpIkmXJjSYNIYpWJqG1L6XpKcnaU7fZ79488Vc2ZuqNrDNWXXE9EdNVxy8RgVXXGxPrLNeT9pE9BC9GTaiRsxDGlgmHGSZpw6TxPXVd7O0uDqBdsy7AIFO0dQC1+SgvGCl5nX3Lolei37xp/j8OQ+tpftpjfTQVfmHHXeJpYF17yhMY9NpDn0ejdul8ZkPMNrh7o43z7CjTes5Obdq9C0UqGdSue55abVRdtWoWDyyv7zuN06yUSW8+0jCAHBgAfTtFFVJy/qxb1naW4qp7IyyMRkhmjE9wtjfP25FSIXQkqLnsQ3KNiTjGSew6c3o6AXizfNQMGvt2LYcXQlTJlnG8PpJ5DSYjr2LmcNcjb216yv+us5AsQpAmVzNj7Gt9oPc3pyhI+t2MaNtUv4T3u/d9n9tmybw+MDrIhU4tdmAtxsaSKEij2V5aoKjZyVLv57WhDk7CxD2T5OJg5zOnkUwy7Q5FtCubuakdwAw7l+nhr+4WX1yZY2pxJHiBsxlgZW85GW/8SkEeNE/HW6Mmexpc2HW36LGs9cbWs2UmYSwzYI6pGLhqVfiPkEiBCCak89q0ObeHLoB7T6V/DC6OMYtsG1FbfMYW1bLCJhH9ftWoaUkh/95DCbNjZz790bEUIwOpbE7dKIRPwIAYZh8jd//xibNjZzz50bUBQFt1tj9w0rsSzJTx47yto19Sxpq0JKySv727Esm0LBYv+BdlYsr+WxJ47x8A9e4w//y10sX1rzhvr8dsMvhBCRUjKe3UfG7KbGvwevVo9lZxnI/JAK73V4tZmPXQiBS42QNXvw663oSpScOUzUs2VWiwqaEkRXQiXGVYBDY/186ewBXhnu5vqaVv56533U+0JYUmLP+vx7U5PE8pli4BRAdyrmcKImY4Rdzkf/6kgP/3RiL7vrlvAnW24j6vI62wvFhUvxYEkTKW2CWhmV7kairmpSZoIziWP0ZbuIFUbRhE69t5l31n+IGk89HtXL17o/TW+mk1b/clYE58aWXHQ8sZk0Ykwa43hUL+XuKlr9y9kY2U7OypAyk1QugpBosjCOKQ2q3LWXudWYX1NSUNhdeScn4of4ctc/MJIbpMHbzMbI9je8qiuKwOPRMQyrmAQ5HUxYKJi8tPcs73rnVkCQyRQ41z7Czu1LS76nhfqcTufp6BpFKPCff2cPXq+Lva+cIxz2UlsdeUP9fTvi516ISCnJmN30Jr/FsujvMZR+FAG41HIqvTdwfvKfqPLdQpXvpqIHRkpJsnCGsHsdEous2UeULcTzhwGVvDWMYSeI549i2AmG00+wNPpbuNVq8pbJa6N9fHj5Fn515Q486pSBUkrsWduEs/FRXhrqZE20Bp/mqPIKgv+59Q50RaUvPQlApTfA/9hyG0IIJvNZolPCJeqqIaBFmbbXmHaBrJXClAZSSsrclTT7lxHQgrgUd4mBc7qOjADCepRqT91ljaktbTyKB4Fge9luQloEgFS2QNDrx6c5Wc6JdA6/x0XOMHFpapG/Y9owOZhz3LdN/iWX95suIESEENR6G9lZcTNPDf8AVWjcWnM/XnV+z9CbRUVFkNeP9LD7xlVUlAcYHIpTXxvhhutWXFJo2bYknsiyYV0jJ08OsGF9I7btaCfvvG8LgcDcoLmfV/xcCxEpJXlrmK7JL9AW+SR+va14TgiBR6ujLfJJjo78ITlziKbQ+6YEiSRVOE9z6MOYdgJL5gm6liOEBigoQkNTfPj0ZkAQdq9DU5zM1x3VzfzbDe9mWbgCd0lkqsS0baYX3DK3j65kjAqPnwfb1lPlCVzUDTu731JKBAKP6idtxgloEVAFQb3cERaqmDcq84JW38CIzsV0l6WU7DvZzQ3r24rJZQfO9FIW8pHOFvB5dFqqy4hORf1KbHoyHbgVDy2+pZelKThCZP7+X3gubSanguaunH1BSolpOqn9qqZw4LUO9ty2jlOnB7jlptW43QtPGykhlcoxMpokky1w7a5l/OBHh2hrq2RiIk1sIsWmjc2/MPYQ+DlOwJNSkrMG6U1+m6bwhwi55hrWhBD4tBaWRD9Fd+LL5MxBAEw7iWmncKllJAtn8OlNuNRKXGoZLjWCpgQRQkdXwrjUCC41WnQXK0KwtqzmAgEC05ErTQEnXX9jeR2fXLWTL505wCee/w5D2eSC79GbnuQLZ14lacxke7anDlOwc2SsJN3pExydfI7XJ54ib2cWNz6LHsmLw5aS8USGF452MDKZ5lT3CLmCSSpboDzkBwl+rxPZOhafCYRLmyn6Ml00+Fou2/UqFzCsSmlzaOJlXhx9glb/coJamB/0f71oG3mzmbJSSvr6Y7zw0tmiB6Y86uf06UEsy6ZvYIJtW1rnDVo0TYtCweSpZ07w1LMnOXmqn4a6KOVlAdava+S73zvIw99/jd03rMQ/D+XDzzN+LjURKSVZs5fJ/GGaQx9EV6ILSnYhBBXe62iLfBJlillLEW4qfbvpTX6L8ew+6oMPFNu9FAN4wbYcjeMC2NLmEyu3s66sloxpALCurIb7mtfwk97TJAo5QvrcKnNn46P88YFHORMf5eBoH3+29XYqPQEiehWmbeBWvHhcPpr8q0lMZfQudoyuDCRBrxsQlAW9SCRV0QDZvOFwqE7lhti2pGWK70RKSXfmPEkzzp2177ps9+58IkRKyfH4Ib7f/zWWB9fynsZfoTfTwde6/4UfDnyDvkwnt9e8k0q3Y6xc7EpfKJh0dY9x8FAXhYLJ6FiSnduXUl4WQAiorAzh8ejEYmmqKkNEIqUkULmcwaNPHOXVg51Eoz5uunEVLc3l/NO/Ps3dezYgBGza0MzjTxyntz/GJz524y+UFgI/p0IEQFej1Pjnc7/O/QAVoVMfeLD4b1XxUOW7lcn8a8Sy++lPPkzBGqcx+B64hAFw/0gP3+86Tq0vhFfV54Sxn5wYxrJtXh3tZSSbIuLysCRUzpN95+Zca0vJcDbJ8nAly8NOzsuTfed4aMkGAnoE03aEkSUt8laGgp1DYrMYBfJiOSmXA0tadAzFWNFUyanuEZCw72Q321c1oWsqhmnhc+sYpo2uqVOJZxaHJl6hztvIuvC2IvVg3s6iCd2hcpwK8DOlMeeZ9hSPyux/H5ncz/f7v8amyA7uqn0PXtXHqtBGHmj4CP/R+yUOTLzI2dRxtpVdz5botVS6ay+aBjCNyckMf/dPT7Bz+1L+5I/un+N6vf/eTeiayqHD3Wze2OyMiWVj2xJNU1BVQV/fBLfetJrrr12Orqvse7WdupoIy5Y6Gtjps4NkcwU8Hp1HHz/Kffdswu2+/BpDb1f8XAoRIQS6mEtNCI7KO98EupDJK210MJp5ntUVf4plZ2if/Fdq/HegCi+2nD+1XQjBtTUtbKtsLMngnQ3DtvjauUMcGR/g+tpW/vP6G/nauUN8YNmmokdmGofG+vjSmQP87robaAuVwSxfjiZcZO0UNhaGnWcw144A/FrEsZFcApIZbemNaiUSJ0O4JhrAMG0sy2b9kjoGYwnHeK2pqIrArWsoYqa0wnBugI7UaR5s/BjeKfrGnJXlP/q+wKQxgVfx4tdC2Fj0ZDpwkhdncZdiF6NFC3aBfePP8mrsBe6qfQ9boruKZNECwZbotahC5bt9XyZuTPDU8A95afQpWvzLuLfuvTT4Wi76jpWVQf7s/34n5WX+ItdtLJYikcyV5Ml0dY9RWRGkty/GK/vbicVSfPwj1+Nyafz6r95UjCfp6h7j7Llh3v3ANizL5oWXznHqzCC//Ru3YUvJX/3dYxw+2sN992xi1YpaQiHvz70w+bkUIgtBFvNpFp40UtokCidJFs7QEv4VdCUMKqyp+DMMO85g6scMpR8l5F7DfBGvqlDwanM1AYfgJ8c/Ht/Lt9oPc1/LGv5gw25Cuodd1S18/vSr/Pba63GpqpN5mk3xv15/mlMTw3hUnT/Zchsh18x2R0FhNN+LJU0U4dD0hfVKvOriGMftKWH60tiTnEwcXtQ9s95manI74xkN+pBScstmx0DaWBkBoK22rDgB6iocQ6+NzYtjT7Ixup1VoY3F817Vx3saP0FvppNnR37MgdgL2FOCLqRHiegz1I/TmkjKTPBw35fRFJ2Pt/4ele6aORNOEQqbIjsJaVG+2/clhvP9bCnbxa7yW6jyXBjRPBdCCCov4MotFCyy2YJDmTmFa7a2FbdtWze3FLdyQgh03flNu3vGOX6in/vv3cTA4CTHT/ZRVxvhEx+9oUh69Ud/cA+f/eLzfONb+/j1X72JUOiNxbe8nfALJUQA6gL34VIXTm6SmOhKmPrAO0qS7lThQVU8NIbeh19vJeheuehn2lJyPDbIXxx+lv0jPdzbvIr/e9OtuKfcvzfWLuG5wXa+eu41PrJ8K0kjx58cfJycafA3O+/jprqlczhIXKqX1eFdTp+nluWslSRvZfBdhGx5Gk4pBsH68DZWhtYv+l2c59kkjEnGCyMYU9uN6QkzG7MzUcVUP9tTp8haae6p/XhJiL6TquBlaWAVTb42fjDwdfaOPY1bcXNz1d2EZgsRHAFoSYsd5btpDSxHYX7Nb7rtJYGV/NqSP+B44hDXlN2ArrjecM2bmpowNTWX8n6VYlpz2nP7WlKpPIoCd96+DtcFpVHLyvz8/u/cQT5nEgi4f+61EHgbC5E3lKaPIOBaeolrdHS1joXsCorQqPBdt+g+jmRTfP38If6j4yitoTLKPT5qfKGiAAHQFYXfXL2L39r7PUzbojc9yfJIJf/Ptj1UePzzvqMlTeKFUfxamLgxRkSvxK36FlkC0+FnbfK1siSwsmhsXCymw903RZxM3sWiYOc5kzzOfXXvx6vOH9YthMCluLmj5gEMu8DW6HUsDa4uIVGSUlLprmF31d20LbJUhRCCiKuca8tvdf5N6fZ1vusvxHR2+EIpExeDoghamp3Fyx8cIRjyoyrON2BaMUx7HLfWhhAqLl3DpV966hXMAWyZmbrv7etIFZfYL/9M2GWdNP0cfckE6yrfTGbm3HZ7EnGe7+3kg2s2XpL/Y6E2JJKBTJKf9Jzk8d6z1PlDPNi6ng3ldXzgma9zfW0bf7jhpjl2mP0jPfzeKz9gU0U9f751D2XuhfMnLGnRmz5J1k4T1KJMFIYp2DmafKuo9DRetI+2tOlKn6PW04Bngcl8qXeMFUYJ69E59Wwufp+NIQ104brkPc442rOKd89cnzQmsaVFSC+7Iiu1lJKscRRdrUMRbrKFE/jd2wGBLdMgTUx7HJfWRDL3AkHPTW9q0o4lv4gtc1QGfx0hBDnjHIOTf0Fz+T+hKN6pdzcQqPNSUEwjnnmCeObHNJb/9VQM088c8/4Yb4uezca0APmjF55kfWXNFRMi0wLkd5/+MW2RMj645vLbsKXktdE+XhrqZCyXZnW0mr/eeS8N/giqEOQtc871SSPPqYlhDo8PcFPdEv50yx388YFH+cQL3+E3V+/i2prWmajXWX1Nm5MY0iH4zVkZqj0t+LXwvKnzF0IgaPUvv4zJL+lKv0qVZxl+zZm409wfF/YrVujBrfjxaw6HrGUbjOW7qPS0oQgVl7iQsc3EtIZw6BZyON4v2+mlUMkVjuL33lpMK5BSopivI+0J0N+5qP5f6t1MaxgQpPP70ZUKFMVHpnAIr2s9IMiZZzCtcTS1CsuOYcskCv43MXEVVCWKoxGCrtYgULBkCtvKYckk8cyP0dQqor53LiiwhFBQlRDTHkMnBSOFIvxvK83kbSdEkoU8f/TCkzzafpZ1FVdOC0kZBf7b84/z+vAgreHopW9YABG3hw8s20SZ2496gZ3Axgl9TxbyPD/YzrMD7ZyeHGFjeR3valvPklA5y8KVeDWdPz34OL+193tsrqjnvuY1bKlspNobwKvpqEIha01XvJu/Mt7FcMmQbGlSsGf4UKW0OTn5JK4yH+qUlyRpjJC1EjT6Npa0dzr+NA3+Dfg1Jyt40hjkxZHPck/D/41Hnc9jJlCEH4RKOvcamlqNbScRqHjcm/G6dyJmVZWzZYJM/mW87u2Y1gCaWvemtRFF8aPKAIY1gEdbisRGU6MINAyrD8tOOekPhSPYMksq9xI+9xZ09fIT5JxYIw2QxNLfRlPKURQvIIhnfoLPtR7DGiHo2Y1La2K+xd0pFeLYtAxrCLCRUiFvnmdw4n9RG/0jPPqyNzUmVxJvOyGSM00GU8k508bJdyng1bSSlPvFwqvp3NG6jMPDg2+4b4oQLAtXLnh+JJtiJJvi4a5j/LjnFHc0ruDPt+5habi8JLfl+ppWvrD7If726As80X+W/SM9+DUX9f4w68pq+b31N5C0Rh1GM8UNSMbyfYzku6nxtOFWfOTtPG7ljRnmTGkQy/eiCadgly0tDJkHFBLGMKbt1JpRhWtqy6GSt9LkrAReLUKleymj+XYq3K30ZY6wKnwLLsVLwc6St1IEtIpZCWoqlj2BLdPYdhxbeLDtJAiNgnEOIXQ01RFItiyQSH8Pv+cmVLWKdO5pvO4duLSlC668EolhG+gLFhm3MO0YlkyiCC9CeBCAYY2iKmEsexJFuFCEC5fWhGXHUYSP2fFCi7XPSSnJFA4DkDPOoCoRwt7bUJUwUhZI5w+C0BlP/TtVoU8iZQ0IL1JC3jyHLXMIFHLGeTKFo/jd27BkCtMex7LjjCW/TGXo13FrLZfxa7/1ePvoRFOo9Pn521vuKtnGSCkZSCX5zSd+yPfOnWQsk5n6k2Ysk2Y0k2Ykk2Y4nWIwlaQvEadzMkb7RAxrKrpUUxQ+uGYjv7V5xxuyhSwGZW4fS8MVtASi/MO17+B/bdvDsnBF0TA4XeBKCEFzIMr/2XEP/3rdu7ilfhkVHj+/t/4G/mjzrVR7gzT7nP3WdJRqnXcpLf51hPRyYoUJ/urMP/Hs6IvkrNxlx4G4FC91vtVYmPSkX6dgZ6jyLMWWBvHCIK/HvkeFu4V63xqUqT17rNDDkYkfUe1ZTqzQw8ujXyZtjjOW68SvlTGYPc2pySd5ZfQrJTEqAAWzw9E+hBdNrUYIP5paXdzqSCmx7RSp7GN4XBvR1GoyuRcJeu8hkX6YWPJfsKzY/C8j4dHBp3hs6ClSZnqesVBxqfWY1ihefT2mPYKUWVxaPYoI4XVtwrJTeF0bsGUBXa1FUkDMWl+zVpbHh59mJDdaTG6cD5YdI5F90rlXWlQFP4mqlAEqfvdWMoXDDE/+FXXR/wu/eyfp/H4kjvdLFSF0pQpNrSZnnEYVfgQqulpHPPMYmfzr1IR/H797O+JNEjxdabxtNBEnAKxAbyJObSDIgyvWkJmqqDacSfF7T/+EVwf7ODjUz1+5Xpq5jxljpy0ltpzeO0p8us7X7n03y6LOSqcpCptr6uhOxN+Sdwjqbv5s6x34NJ1Gf4R4Pu/YSkwTv8vFq319rK6qotLveGR0ReXamlayaYt1m6sZS2Xpn0ywqrISt+plTehaMlYCz1TB7mn4NIlbdfGlrq9zIHaI9zS+kxZf02VrJYadJWclCeu1hMNOTIUiNFyqD31OeL3Aq0ao960lb6UJ6zWM5jpYHrqRBp/jQjbtPDk7hZizNikI4cK2k+QKR5HSwpZJNKUC5xO0MMwe/O7rUZQIhtmFEC4UJUp5+HcpGB0IZeFM3YyV4Tt9T7B3fD/31u5hU3T9LM3EJmecw6OvRFMrmUx/n4jvPiQGkgLZ/CFMe5Rs4TiWPYnHtYascRJllm3HRvLY0FM8MvA4uyquYXfl9dR4qkq0SyklsfR3UZUAoOB1bUAIL1LmyBqnSedfIezdQ844w1jyi7i1NpK5l/C5N6Gr1eias3WyZYG80UV1+HcwrAE0JYqUeYK+u1Avkt7xs8TbQohIKRlOp/gvzz7GoaEBlpWVc0NjC37dCfbJmSbxfK7499nlGh2H3DxtAhnT4JnuDmwpiedzXFPbMOvsJSI5xeVHGQghWDG13ZHAD0+d4oaWFg4ODLCmqorjIyMsr6hgOJUib5rUhUI8cuYM45kMty5ZypNn27m2uZlkPk8in6fM58M/T7auR/Hwwab38Jdn/p6j8RN0pXu4v/4ubqq8AZeykFo/T3+Lk710HFShz4mxcCbMzL8NO4ciNNyqHxuHK9XGWpAFzrSGcOlLUZUIBbMTl9ZGKvsoHtdmQMWlr5o3gVLgweNamD0NwKW4kEg60938S/vnWRtezf11d7Ik0IqCxKU1YNkTmNYIHn0Fpj2GZSdwaS1oag1R10YMawivawMFsweX1owQM0JUQUETOqPGOD8efIIXR19he/lWbq66gXpvbdHtXhZ4CEV4iKW+haZWYMs02cJxFOGhPPAhFOFHYpItnCCVe5mw744SjQcgb5xHER7c+lIMawBQ8Lk3M5n+HhXBX+HN1RR8a/C2ECKWlPzdwZd5qa8bTVFoCIbxajMqW3Mowt/cche//eQj/Obm7URmFUz60rFD7KhrZFXFjK3i+Z4ukoU89yxdQbnXx+ePvsbLfT3cuWQ5w+kU52Jj/H/7Xpg/GEk4XKiryyvZUdeIT1/8pARKViYhBMeGhx3SpEyGJWVlKEIwkEjQOTnJTW43I6kUyUKBF7u60BSFR8+eRZ0Kv757xQpao3ONwEIIajzVvLvhHXym40skzCTf6PkP2lOdvL/p3UT0+evezH1VQcHOkDLHisfS5sT8cRUIksYYA5mTGHbOiVz1b6ArdYCh7BnWRe7ClibqvLlHjjvX69pKwTxPwHMbQriRMotlJxamSFhkiv9s6kVTmhyePMqZ5Fl2lW/nXQ33EdZDqIpj9HVppe5xVWlFSolLc0pReHQnyHB2nxShoCszUyVhJnly+FleHnuVrWWbuK9uD9XuKjQljJQSwxpBV2swrEFsmSFrnCRbOI6qRMibnVN9sIn47i32Cxx2von094n6HygRLl7XWmKpb5PKv0LAvettp4285UJESknSnCjmRhTsHGG9omQgTo2N8OP2M1R6ffzeNdfyzmWr+e7ZE6QKjotTCEFrOEJtIMi1Dc1U+wPFtp/oOs/G6lqubWgutjeUTjORy3BTUxtnYmM82XmeyXyOzx05WLzmdGxm4swHTVG4o3UZf7H7doKuN0YgI4B0oYCmKExmswghSBUKhDwegi4Xo+k0N7a28nJPT1EwRjweBlMpFCEIuhd+rhCCbWWbOThxmP2xg1jS4uXxVxnNj/PJto9Q65kbIj5PI6hCQ1Nmwu01xV3UOEoNioKAVk61dxl5K0176mUECjXelewb/ZpTnkqaKPPu1210tR4hPLh1J52gYJ4lmX2MgPf2ea63AHthNfMC6PMxxkuJS3GVlBK9FBYyoCpCzNuOEOBTPRcUy7IxrWGyxglsmSLsu4dM4ghu1yZ0tZZM4RAR328wTTY1vdg4RtmjSEz8nh0lfRC4KA9+kP6J/0F9NIhXX/e2EiRvvRBB0pE6SoNvOQB9mTNsiNzI9NdhS8nDZ09S7vXxf27aw5aa+itq+Nzb10NDMERbpAy3ppEu5BnJpFleNn9ofM40OTE2Qtoo8GjHWa6prefDa99YPRN7ihdDEYLlFRWcGBlBFYKsaaKrKtWBALqikMzn8btcnBsfp9znY3l5OSdHRnBdwgulCY376u7kROIUKdPh8jiXauefzn+W/7T015x9+zzcF4adxcbGsHPoihefGpnVZ3Mq4zZNb/owYVctle42RysQCqrQp9zAzoTzqRF2VX0UgYolC2jK3EAzj2uj4xWZEkbOyr+UsP896FrrHAoGVa3E5yktjXExXDjBK1zlfLz1g6wNr0IgiBXGiOhl5O0cqtAw7AKKUPGqTuBX2koxkhsCoMHXjG1buJWZWkMCZc4ztkY38UD9vTT66ksiai17EiFUKgIfo3/ij1GEj7zRTpn/vY5XyiksMhWEdnYqAM6PZU8SzzxCZfATJS7vaXj1NZT5HqBn7HepDv8nQt7bpuJFfvbC5KeyndEVF7a0pvInAsxeXuL5HOcnxvn7W+9mfeUiVs/LxAfWrOcDazagKgKBYP9AL98/d4r/feNtKPMsc5aU/OW+F/jc0dewpeTxzvM8tGo9LlUlUyiQyOepCQaLd8ayWYJuN3nTZCCRoCkSwas7H0G5z8dYJkNDKETnxAR50+TQ4CBZw6AlEmEyl+P5zk4MyyJtGKytrublnh7eu24dh4eG0C9RJU0IQZOvge1lW3l65Pni8e5ML1/s+jq/u+xT+LQLDaSSiUI/Qgi8apBa7ypOxp+gyb+FrBUnZyVoC+4gYQzjUQOzDLr2LKb42YzxAs+USl6wMni1uTYcTZ1bEApUXNpyxgsxTibOsDGyjqDmsL+pShhVWXzuyoUTvMXfxPrwmuIK35/tRSA4Fj/MyuAaBnJ9BLUQbf5lJM0EL449w/rwZs4kTzBWGKHMVcES/zJmsqpFSSEygWB35XU0+UqJqqWUJHLPEZpy61aGPslE6rt49FXoajV5mZyqa+QEoeWMs6hKBCF14tnHKAu8D12txbInsWUWwxopThUhFMoCDyExmMw8gkdfjke/uK3op4W3XIiY0nAMm8YoLsWLXwtPlUBwJppH0/jzG26lORS54gLEoUgsleqKEMWPYj6NR5ESdWrybqyq5U+uvQm3qjKWyXB0aIhYNsvWujrCXi9Rj4ehVIrBZJK8aVIVCGBNu3GBzXV1HBkaomBZVAcCbKqrw6/rvNrXR9DtpiYQ4IE1a3j4xAkaw2Fe6enhlrY2VEXBtu1LaiLO+yjsrryOvWP7yNkzFAanEmc4PHmMneXbSlVjoVDlWYpEMpbvoD35CrriIazXUOluZSh3hvbkKwS0ctZG7sSlOGHzEkmiMER/9hiGncOQOaZtHcPZsxTsDL2Zo2wpf3CeXi6MgewgX+3+Jo8MPs6tVTeys3wbAW1hKsn5cKEQmX2nJS3qPI24FDeWNKeMtQJNaEgkeTtHjaeOaW66KnctKTNBykwRnCq2JQRzcpYuTEiUUlIwu5GyQMBzPaY9SiZ/BF2rpTzwYYRQ0JQKDGuYgYk/halweF2twauvJuy9HVUpmxprg0T2SUYSn6Ym/HuznqlTHvgIZf73ztLsfvZ4S4WIlJKUMUGNp5kJY5jpkoqzf3SvptMcisx//1vZuQUggYFUkrpAkL+86Y6iezhVKLC+poZDAwMEPR4yhQK6ojCaThddtt2Tk2iKgl/XMWybnnic65ubyVsWx4eHWe/zcWhggH29vXzqmmtwaxqK5Who/fE4W+rqmMzl+Id9+1g6ZYRdDBp99SwJtHIicbp4zMbmXKqdneXbSt9PSjLWBP2ZY9jSYnvF+wlo5cVgrnrvOirdbbw69u+cT+5ldfg2AFyKj9bAdup96zDtKVsVTiHziKuOg+PfJqRXU+luY7EQQrAmvJqHGh/gGz3f4Svd3+TpkRfYU3Mz28u24lUvzbUhhLhogp4lTboz7VS5p12oNm7FTcZKIxCUuyrpz/aiC52lgZX0ZroI6eFiEXHnPcUiw8wFUf8DgKRg9hHy3jhlC3K+d1WJ0lj2V9h2aqrvLkcTEQoKM4xpulpFeeADuPWl+F2b5ywCsz1Hbwe85ZqIrrhRpYpXDaILF4pQiRWGKXc7cQm2lOwf6GV91Qwr+mzM5yl4I8cWK7UN26I/meDj67ewLFpeVIkjHg/tsRjLKyronpigJhgk4HKhKQpZwyDkdnN0aIiG0NTqBWyqqUFXVXy6zvXNDjnvptpaVlRUEJgqhq0pCvevWoVf11GEoCYYLGopi+2zJjQ2RdaXCBEAtzK/YVYTbtoCO1DnSZQTQuBWA+yq+hjWLNaxMlcTZa6mqVXcRYt/RsPxamF2VX3MmWyXGb+oCoWbqq4nbxf4du/36Mv284XOr/PsyEvcV3cnGyJrL8lQdjFh61Y9+LUguuLCpTgFwMYLYywLzFA9pM0ULp+b4fwQNZ5avKoPTSmdGvNtfWdDCIFbnzHu+92b571GFQFUJXDJKFghNIKeay/6zLcL3nIhMs3CFdLnN2T2JxP8l2cfY2VZJa2RSPH4qbFRCrbFeNYxGJq2TedkjL8/+DI+fUbYHB4eJJnP82xPR/HY6fExcqbJRM7JDynz+PjY+s0lbuOFkMznUYTg3qUrZ4VuCzRFIZHPs6y8nOFUCnXq3GQ2iyIEKysraSsrIzTlUbkwND9v5TmVPEulu5x670wJByFE8R5whE9j+PK4LIQQrAguw6XoFKYoFV2KzvrI3D2zIyQuXWJBFVpJgNts13XGyk7RBcwY9haTGLjws1TuqL6ZlJnmkYHHsLFpT3fyT+f/jU3RDbyr/j7qvbULT7iLCK6MmSaWH0UVKuOFMZp8rSwNrKA700GFuwoAv+ZnLD/iUDbi2PAu9Axdya2DlJKR/BgD2UE2RNYuiurg7Yy3VIhcOhFM8q1Tx+hPJuhPJqB77jUHBvtL/v2Nk0fnXHNmAXftwSHnXo+qsbW2flaw2cJIGQV2N7VS6SudaLqqUh8KoQpBfShUdL9ub2xEVRTcqkpDKETUO78KPpgb5h/PfYY9Nbfy7sZ3XLIfl4tabzW1nhq6M06tlx1l21gWuLxSDZeClJKsleULnV8jY2X4ZNtHFx2TciloisY76u5iojDBi2OvAGBIk1djr3E+1cF7Gx9gR/m2+V2tF9ESslaGlaG1mNKi3FVBb6YbS5qoQqNg5zHsAj41gGEXaPUvZTDXx7nUKSrclaizpseVKknhCJBR/vn85xjIDfK+xgfZXXXdZbmi3274mQabTeayHB8bZkNVDW5VRVNUNMUxeA4kE1hS0hRyVmVLSo6NDrO6vLLE4Hh6fIxqf4CoZybWYSCVxLCtEltLXyLOtpq5NWwvRFMowq9vumbOJ+NWVZaXO9sb/9RWRAhBhW9mL9sanT8s2SnkNEx+KrHtrYBH8XB79c18o+c7LA8u5aHGB9Cu8IdZsAv8e+932R87iETymY4v8mttH7tigsSluHhf04MM50Y5mzpfPB4rTPCFzq/hUb1sjqyfdwu2EMpcMxpwRfluLGkhkVMGdgVTmjT7Wp0CYEIQ0BzmtQsr9l0JITItQP7l/OdoT3cC8PWeb2NIg1urbkJTLv/3clz2JrrysyN+vuJCxJYGebMLj7YUy550jKlqlPmihnx6gn+49VrcasWU1Xzmg/jGySOkCgV+baNjGMyYBr/+2A/4q5vvpGpKS5DA//X8E9y7dCW76puK7X7z1DEmclk+tema4rGFBti0bfYP9HJgsJ/lZeXc2NSKT5sbpVpq3Jr/7xfD+VRHUYBcuXIOpbiuYgcrgsuIuMJ4puwhV+pZpjR5uP8Rnht5qfgex+Inr6ggEUIQ0oJ8uOW9/NWZf2TSmMlxytl5Xhp7mU2RdfOE5DO1Fbk0g5l2wbYroJXSFzhhCHNLe7xZzBYg56cECEDeLvDNnocpWAXurL0dTSxMAzlfm2P5BD/of5mPtN42b9DdTwNX9KlO4luWRG4v7kAjGeM0IFGVLQjmGvFyheMIxYNLrcaJUpwZQGXKhTbtbp3m7lBmHZsOUpp9DKbdb5Qcmw+WbfO1E4f5y30vkjENNKHwqxu38gfbr79iYwLOCn4meQ6Aw5PHSBjzF7J6OyNppjg8ebRIrjwNR5B8iV+7QlsbIQQtvibeUX83X+n6Zsnzstb8LPzzLVBpK8O5ZPuc/r4RSClJmKW/2flUxxSP7eJg2CY/GXyiqIGUnJMG3+3/EXm7wP31d6MzV6uwpSRj5vBrM65dCXyv7yWOTnbykdbbSvo7UUjhVV141EuzzL1ZXFEhYtpj5M1eTCtGKv8aBXMAIdwMJT5PTfDjgBuH1Upi2pPYFMA2sGWK0dS3Kfffjz6LZPmtVs5e6uvmr199iYxpENBd3NTcxj1LVhTPFywL9QIBNQ17KlNYSommKAvnf0hJT6af/qzDYzKaHyNjZee99u2NhWPQj8VP8G8dX+LXlnyMsBYqjkXfRByvrlPm9xJLZwl4XLi1uZ9cPJNjYDLBytrKYvzF9RU7eTX2GicTZ4rXtfga591WzNcrXWgci5/kqZHnSia7mPofwOXMLWsWBYBE8oP+n1zW/XLqPwtx5NrS5seDT2BJiwca7sUlZkLpC7bBD/peYSgX4zeX3V98XwE80HAdaTNX0tZ4IcGfHfsqNd4ylgbqmK7OOJ3dbkoLTSjc33AtQf3Nu4uvqBDRlAqE7iKVP4RHa8WwxvC51mDaE0xzIOTNXiQFRlPfxKMtwZIpkvlXifr2oCnlV7I7F0Usm+H/2/8iGcPguoZmfm/bLjZU1RY1HltKfnj6NJvratEVlbpgsJgYN57NMpZJ0xGbIG0U2FRTS9Trpdznm/MciWTv+CsUpmIrbqjcxbvq73vL3296ytsSDMtiOJni1NAIO1oaCXsddT2ezRHP5YsRtiAZTqZYUVWJrpZ+7BNGnP996q9LthgAbsXFiuAyNkU2YNmlK/O54THqo2Gifi/DiRSHuhPsXtGKJW3c2sxqOxBP8OOjp1lZWzmrXTd3197BuWQHhjTwqB62lc11m8L8W0qX4uKhxncCsD92kDpvDU2+Rmo8VUT0MG7FPceNu+BYSsm3+x7mfMrRIgSCdze+g6UBJybGsicwzCE8rlVT12fJGWeL9IsgyRXO4NGXw5QnJmecxa01IcTM1smwBlGFRqZwEt29FqbicHKWwSMD+6l0h6e2bDNewzJ3iDpfqedTFxp+zUNnaoiB7HjJOVtKJgpJKt0RNkaXsjbSsqgxuBiumBCR0iKeew7DimHJFIrwksofxDNVZNvJbKzGozdjWnGHQ0GtRtgKulqF6wrQ4C26r0i+cfIofck4v7ftWj66bhN+fUbtk1LSE58k4HKhCMGL3V3cv3IVHk1DEYLzsXEKpkUin3fC9mMxGkIhyi7wzEgp6cv2s298JvFPQcWnzRU2VxI5w+S5cx1UBQLEsznOjY5THfRTG4ri1/14p1Tc9kSCI/3DrKurLo7LC2f6WFNVj0+bWQmllOyLHSRuJIrHBIJlgSW8q+E+VgSXzonlyBYMUvkC/RNx4pkcYZ+HmnCAM8NjqEKUCIxUroCiKNhSFl3nQgjWhFays/wa9o7v47ryHbT4L48zxaW4eG/TA9xTdwchLVj0gFzud+a4s0vr/TT5GlgdcrTWnHGebGGAiM8JlbfsCSbSr1ERXIbEwjD7STCCz92IS61zon+zMSK+69DUCKY1iWENkjNigEou/yq6sDDMQcK+uwlqXq6rXMu+sZMUbAOvWhr/c+HbhHQfd9RupdlfTYOvNOUgbxX4ycAB3tGw6w0ZcufDFXRQK/j0NXj1NjQlQtZsJ+K9g7zZi6qEiGV+hMRCSkjlDxJ070AIBbfWTDJ3ANOem4L+VkWsxrJZ9g/08rc338WnNl1DwFVKM5gzTfb19uHTdV4fHGRVZSXPdHYwnHYiDW1bMppJ0xQOoysKlX4fhwYHsGb1X0pJzs7znd4fkDRTxeMu5a1npZJScnZ4jJDXTWtFlIJl0lwWoSYU5EB3HwXL0RjmS/h3NARnxZqGIQ32ju0rGi41oXJb9U38/orfZE1oJfoCHCaKEIS9HsoDPjpHY4wm06iKYFVdaWJgLJ2hOhQglSuU3K8JjQ81v4c/WPHbPNT4wCLLZcx6GyFwKS7KXFG0Ke/FlXZ5581u8kYnBXOQVH4vebODrHEKwxognd+PQCGdfw2J4VAvKj6yhSM49ItTCYkYZAvHsKdTCaSFLbPY5JFT1Rh3lK8kbebImAvZhUrfWyDQhYpL0Ur+6IqGOkVtoF6h+JQrpokIIdDVKgrWEH7XBnS1HF2vRAgXebOPdOEIIDHsUUw7Tti7m3juBYRwEfLsYDDxT1QHf6VEI5n9c+dMk/Fshq8cf70Y7Yl0gtIEcHR0qHjt4eEhsqaBIvYD4FJU3rViDRGPs/+LuD38t923U+Gdv5yCS1VZUVFBspAnls1SFwxiT9k+bCkJud0Mp1OEPR6WlVfQHpvguubm4ioqpcSQBg/3/YjDk8dK2g7rly489WYhhMClqfhdLqSUBNxu/G438WyOzY3O+J4eGiVrGLRVRNFmbV22NtXzalcfNaEgK6odNbkj1UVHugsAXei8q+Fe9tTcWsLjcSG8Lp2g203Y50WZMn773S4uWCewpWQonmJzcx2nBkfY3tZYEuTn03ysDS+caOYUWZ9bz3cxuHDRmo5Onv77YuBSG8nI17HsSdzaUnS1ilTuZYRw43fvQMo8bn0J2fRRhxDJGsXv3k48++isjtjoah1Z4yQ+zyYKZi+aUka2cIK82YHXtZoWfw1+zcuJeDdNs7QLCcTySbrTI2izhMJ4IcESLl0B8ErgitpELJnGtGN4tCUUzGEMMY5ARWKhCDcOCW0nUd8dFKxBsoXTBD078eqrCHtvJpl7hXL/O4rdmv6JpZT88PxpzsTGODU+Oue5swXIbLzQ2wU4K6JPd/HeVesAUBVB+QICxDmvsLy8nJxp0hGbYDCZ4tYlS8gaBkIIaoNBBlJJOicm2FpXx2g6TXM4UvwIs1aOb/V+l2dHXyzxDigo1HjeOIN90kgRNxLUeWtKqPnmwomD6BiLkSkY1IdDGJbFU6fb+ejOzeiKwrKqcg509yMlFEyLwXiS1ooo50fHeefG1fimBLVTinMfBdtAExoPNtzPnppbL6kKT2ay9E7EyRoGAY+bqN+LbTu/aDKXJ+B22h+YSFAe8LGytpJvvXqUtsoyKoOXTnGXUhI3E7w0uo8nh5+9aPyNZdlMprJkCwamZePRNVJZR+upLQ/hcWmc6R1hZVM1Q7EEBdOiufrSVITO722SLRxFV2tJ5V4i4NmJLTPoahUgphLlNAxrCMtOo4gAqfzLANgyjZQBEAqGNYx3ihDJrS8lZ5zDpTU6dhTAr7lp8Vfzl6e+jesCW07OKvD40Gsli64mVLaVreCngSsqRAxrBJdag65WYdlxPLpjXHVpDaTyBwBB0L0VUNAUJ0Xcoy+ZCv2+FtylK0CqkHe4K3NZHu84R1Mogq4oqMpMFu6FtH1SzpRusGwby7YxbJu9/d08sMJZ0Uz74m4/KaUT3DYyzK7GRg4M9DOaTnNwoJ/7V67Cp+uUe30MpZKMZjKkCgUyhkHQ7caSFt/u+x5Pj7ww58OOusI0+i4d8LZQn86l2vlc51e4oWIXt9fcTFSfP/M5Z5i4VBVFEZT7fbzW08/aumoqAj58Ln0qh0OQyDlWfVVxBE5rRZSJTBZVmclwHs/HODx5DAWFu2tvZ0/NLYvaSydzBbY019FcHuWpk+e5fnkLZ4fGyJsmx/qG2NHWSDyX5+zwGDevWoKmKOxe0cYXX3qNu9avYHVd1bxeMYfkKsUr46/y5PBzDOdG8F4irkMIQd4wOd83xsrmatLZPP2jkzTXlOHSVYYnkvQMTxLyezjWPsiyhkqGY0kqwn40beF3dbYznUUDatBzA+n8fiK+u0hkn526ysQwB/C7t+LS6rFlBo++gnjmJ2QLJ1A9ITSlHFXxo2v1pHP7CPn2EM/8hIjvXqZr3wgEq0KNlLtD3FGzZaYPwBNDr3FL9caS4MJDE+d/akSKV1SITBtRkeBzrZuK8wgipSTo3lnywasiQIX/QRAzxMoliVQS9g/0kbcsQm43/3LHfajCydicVo+FmHbZldxW4s6ypwSClBJ96uOfyGXnPm8WMobBRDbLtrp6vLqOqihkprwwAhjLZKjw+VhfXY2mKATdrqKdoT87yIujr8y7Mu4q3z7vdma2Cn2xpMGudA9xI8GPBh/jwMQh7qq5nZ3l1+BVZ8UOSEnfZIKW8ijguCYLlkXA7cIzq3SjlBKPpk25qWeeEfF5i6z0AK9NHGbSiLOz/Bruq7tr0R6NxrIwpmVzZmiU7W2OR6ixPMLzpztw6xqJXJ7heIprlzbjnupXfTTEg1vX0jk2wcqaSmY7iKSUpMw0+2IHeHL4OQayg/hUL/fV3UnUFeVLXV9fUBuZXkwmU1kGx+IMjCVoroly5Hw/VWUBjpwfoDzs53jHICG/h2jQy5MHznDXztWELiJEQCKEB69r9RTRc5yA5wacIl0OTHsSn3szhjWEW18KUqIoPjSlDL/7miI9osO5Ih1t3hqZKm+hF2OhhBC0BepImlnaAjN5RLaUVLhDtAVqSygcezNzNfa3CldUiBSn8wVzc+FMRed41jL4xomjfHDNhmIcga6qXNvQhKYoaIpC1HPl0p97kwnSRoGwe/4VzO9yFUPbAZaUlZWcvzBBriUSnblX81HujhbjQsDZxmyObuDu2tvnTbayTJuR3nGqm8qxTJvTB9tZtrEFoQjcXseTYmNzPjWTZDiUG+FLXd9g7/g+7q+7mzWhlWiKIxS6YxPcsLQV07Z5paOHFdUVnB4eddzTqQz1kRCmbRP2enCpKvEpjUQgaC6L0DsRZ1lVBQW7wMvjr9Lqb+b9TQ/iLqEBvDSKRlSc37omFOCha2aKi4e9pZwYQgjaKstoqywrEYopM83+2EGeGn6OvuwAAsHq0Are3fAOlgRaOTjx+kX7oSgCv8cNcspepKt0DsbwuBxC6mjQy2Qqy9rWWgzLwpaSoM+Dx3Wp6SFwqY3kzXZAwaU1Y8sUE+mHixX9HEZ7p3aPKoKIqTABpjSM6QBNVQmTM84R8d2LInxkOUYy9wJh753Fp9V4oxyMXTrAzdGkbYbzk3O+t4JtEjfSDGTHKXMF8WlvjPpzNn7mRM1SSh45f4Z/eO1lWiNRrmtoRlcUbm5uI+R2o10i6vSNwCl5ufhow8tBuauM/7rit9k3fpD2VCeaorI+vJZtZZuLoehz+qMpxIYmcXt1YkNxTu4/j2Xa1LZWUd3kxM4kjRS92dJkRBubM8nz/HDgJzT7GglrIUZTaTY21FEwTc6MjNMYDbO6tgpFCCoCfvZ19vLOjavpiU3i0R2XtUfXaYyGURVBbShA+9gEXl2nOuzjlqobafY1vqFo1OlUhtn/Xsw9MLNt2Td+kKdHnqc/O4BEEtZD3FO7h5uqrsMzRWF4qch+VVHweXSCfjfLGysZnUyhqQq65mz5BscTVEYCuF0aftXFkfMDjrt5EcxyAC61Ad3rBMopBCgLPIRgxp0spULEfz+zV9eI726nMiAANppaRVhrY5o6Mep/cMpbM4Ow7kcR4pIE1pa02Td+mlfHT6MI5QJN3cm1+VH/Ph5ovJYPtdz6pj1WP3Mh0puM84+vvUI8n+e3n3yEbbX1PLBiDdc1NOO6Qn7s2ajyBfjLm/ZQ6X1rYjWEEFS6K7in9o6ien0xI6iUkvhYkkzSIXJWdZVAxI/X7yYdTwPlSCnpzvSWxGmAw8Wxo2wb7216kLAe4pXHj9G8sha3R+fAS6fYccsahD2TQtBSFqG5LIKA4nYHYHw4ztK2Vrw+N3sfO8INN67C53cE3g2Vu96ScVoIUkomjTivjL/KsyMvMphzyKwUFDZE1vKehnfOoSW8VDCAlJJcwcS2JW6XRl1FmIlkprgVXtZYibQhmzeoqwizZUUDTx08i6IsbnIpigdwNA/H3jS3nGjWVtBFAdcUB+3sa1QRmHO9ECqqKM0kdys6PtU9520taZcck0juqdvOXXXb0ISKKS0UBLrisLlZ0sa0LXzalckRekuEiJSSdMFASomqCHRVLfJrSCk5PxZjSYWzRfjumROMZTIInDT8Z3s6eaG3i5ZwlHuWruDepStpCUeLkaRSSvqSCbyaRoXv0rwYF6JtauvxVgW2ObaEPJbZgROGI1HUeiCIlElsqx8h/DiVzxSkTBOIrCKTyiEUhZHecVZubaPrZD/b92wotnssfmJOrsZNlTfw/qZ341J0RgcmsC2bUwc6CZf7Cfo9nNjXztJ1jYTwF995+q3V2dsICQeeOsGqLa3EBuOYBZOh8RRev5tIxXz1da88bGkzmh/jpbF9vDT2CqP58aIQDmkh7q+7k91V181bOnQxmdGTySx1FWHG4xkCXjcrm6o41jHIeCKDbUuaqqKEAx5My+b1c/2UBa/MIiOlZDg/yuc6vky5u4wPNj1UwsMyjUQmRzKTnxNK73O7CPs9U4JH4Z76HXMIkpJGhrxlFL02uqKxITrDMDeeT/Ds8GHuqd+BR728LelicMWEiGFZSClRFAUkPHz4BIf7HbvAzcvbuGl5W5FM6D9eP849a1ewtraaX1m/ldtbl3FoaIDnezs5PDzIeDZD+2SMvz/4Cl8+9jo3NrXy0Mq1bKqpI2+a/OFzj1Pl8/PbW3desYCZaWOsYdsYlkXesrCkzfrKmnnzPRaGhVF4HcvscgxjmGAcxeN9EGnHse1xVNWLbY2iqo1YVicu9xqkLRkbiDE5kqC6sZxCtlC0h2TMLMfjp0qe4lf93Fa9G7fqxIL4g15aVtaSTuYY6h6jeUUtx19tJxT1IyVYljVHAU5OZgiEfVTURmhdVcdQ7zi1LRW0H+8jnciyYlMzbyWcvbtFT6aP50f3cmDi0Jyo2FWh5by/6d0XrfAnuXjRcyEEzTXRqWc6OTNCCDYsdcihasqCxbZ1TeX69W3F+97s+/Vm+/nX9i/QnelFJCFlpvl4ywcpc5W6kNO5Aj2jkzMdnCJzPtc/xgdu3oSmOsmpIb1UuAmg0Vd10XlQ5gpS4Q7zta6n+VDLrbjVKxvweEWEiJSSR46f5qX2blyailfX2bN6OXesXkamUODMyBj/48dP84e33kDU52EomcTvck25dt2scVexuryS961ez1A6yd6+Hn7SfpbXhweYzOf4wblTPNZxls01dUTcXl7p7wEET3ad58ql6U2V4JwSJlI61IX/duc7uLb+csKtJdKOAQUUpRrbjgMmttUL6Eg7jS0mkPYkUpmunwPxsSStaxrwh734I77ia01vZYZywyVP2RBZQ63X4Q0VQuALeji89yxNy2qYHE/RJCWhqB9FEeQyeeLjKcQF6nkilsbt0bEslfh4inBZgHQyy/hQnJ23r0N3X3lFVSJBQspMcSJxhpfGXuFU4iw5u3T/71E87Km5hbtqb8d3Ca5V+1JGEWYEwuxmLmXwfzOQUnI+1cFnOr7I4NRvJ3GyuP/h/Gf4ZNtHqZtVG6i2LESuYHK0c9CJGLYlW5c1sKKh8pK2mTtqtxSTQefruxCCayvXcGjiPF/ufJKPtN52RQXJFftKVlRX0hSNkDctxtJpBPCdQ8ewpGRXaxPVwQB/9cyLPLR5PWGPh4boXApAVQiiLi/vXrGWO1uXM5BO8HjnOX7Sfpb2yRiv9PfOulqSM80rHMbstDv9X9Oy+f7Zk+ysayxR/y8OGyECqFoQIfwIbKTMoqh12NYQCN2xzAsN0IpxLnd+9EYnI9il4fW7qWmuRHM5W8CDE4cw5EzpUF3o7K68riQMvJA3SMWzJCbSjPZPAFDdWM6xfe1s2b0SX2Du/remsRzblrz82BFqmytoXlHLK48dxetzYdv2RcdWSokpTfJ2Hr96GfVPJJxMnOFLXV9nKDcyb6p+jaeKDzY9xPrIWpSLBtVNN3nxuB9LWrwy/iq9mf6LXrdw+5KBWd42ieT50Zc4NSvD+EKY0uLV2GvEChNzzp1PdfD35z7NJ9s+yhJ/K0IIxhNpekYnqIkGqY4G2He6h9fbB2itiRa3VqZt8WrsDF2pIW6u3ki1x9GuJgspvtj5BPfW7aDMvfD2887abfz5ia9j2CYfb7vjitEEXJYQSWecuH2f1z1HortUFZdXJWeaZE0Dj66xs7WJnGnic+ncvmoZlm3zF08+z2/fuKtYU0VKiWnbjKcznBwcIZHPc8vyJTx1+jx3rVnBb2/ZyUfXbWZffy/fPn2MfQO9pA0DRQjuXrKC961eX8K5Oh9yVh5d0eeofDmrgD6VOGbZTj9M2yJrGVi2xLKnYimQqDg/ohCOy3bhwXchlCggscx2VG0ZDlO4jpQ5VLUahBchvAgxU4PH7XW2JfVLnIjWTTc5gXFJM8XhidLQ+SWBFpYG2kr64HLpbLpuOadf76KutZJIRYgTr7Zf0qYhBLg9zscUG47j8btoXFLNgWdPsfP2tajaXJKc6bD+7/b9kPZUJ59s+xiV7vJFf5BtgWaa/Y0M5OZGGq8OreBjLR+k1lO96PYupYkoKLT4mvnx4BP0ZPqKx1WhoAsXmqItSljNjvE5kzzPmeT5i1w9957ZSJlpvtL173y09QO0+hwS75aqMsYSaWLJLOtba6mNBjnWNTRFIyB5cugQ/3D2e+hCI6T7uLtuOwD7xk/z2MBBXhg5VhIrMheSpJHle317qfGW8UDDlSGCviwhcvLsIMl0npt2rbhkjoGUkDUM8qYFUyEelrRpKy8rxiVM4/lznWiKQpnfS9Dj5nD/IJsb63BPfcBht4fbW5dyU3MbJ8aG+fqJIzzZdZ4ft58lb5n89503Lli3RkrJw/2Ps8TbxPrwTNFoKSWPDD5DtbuCbdH1JYFeB2IOj+u2svUl73gm2U5vdog7qi9OWqSo1ZjGcVStDdvqQyhBVCRSZtH0Fdj2OKBiWd0lq+h8k/VU4gwj+RkOWYHgxsrrSko3SikZG5okly2w8471HH7xDG6vTll1mLbV9RedHEIImpbXkJxIMzGaZP3OZWi6SteZQc4d7WXl5pY5fZoWII8OPYUlLT7d/jk+teQTixIkQgg8iocPNb+XuJHk5BRDvUCwNbqJj7V+gJAWXLQAceIsLq6JCCGo99byG0s+wdd7vk1Ej7A00Eq9t5agFsStuq84leRiMc3Ib9k2fWOTTKSyhP1eRuMp+sfibFnWgAC60sP8qH8fH2q5lesq11LnLS++203VG2lPDbCjfNUluFolKTNLrJDi1upNV+wdFiVEpiMYpQTTtADJmXZnn7diSTUIgWnbxLM58qZJIpsj6HZzZmQMy7apDPppHx0n5HHzx3t28/CRkxiWjUtTGUqmSBecRLCu8UlS+TweXWd1TVVJH6a1nY1VtayrrOFj45v50vHXeazjLB/78cP88bW72d3YOu/+0bDNYmj07H2jecHxhJliopAga+fwqz72jr/GmtAyRvLjNHprnUSvCzJ158IATHTXVoQIgb7G2cYg0bRlgBtFqUJxlWFbY4gpN958bVnS4qWxfSUqf42nio2RdXPuqaiNFP++6caVqKrCqi0tF+nnDKrqo5RXh1BnRWduu3km6W32/RcKEICzqfbLFiQhLcgnWj/E3537ND2ZPtaH1/ArrR+a13NxKSyGYSxWmOTpkee5rmIHa0OrCenBS+Qf/XQR8LqprwjTWBkhGvAhkbh1jYHxBJGAl9H8JP9t9UM0+irn9NmlaFR7omyMLi1GZS+EN1JG5VK4qBAxLZvuvnEE8NKr7VSUBRiNpWjvHuVsxwjXX7Nkumd0jU/wUkc3inAIfby6zm0rl5IpGOQMkzK/jxuWtiCAtvIyYpkMNaEgVQE/a2qrONQ7gJSSkNfDrtYmOscnCHrcc6rACSHQhGB1RRX/7423875V6/n066/yB88+xq+s38rH1m/Gozr+8IlCHInDzzlZSLB3/DVWBpdgShOBIGlmGM2N83x+P7sqtuBVPXxn+FHqvdWoQmU0F8Mb9ZCz8jw69DxLAk0gZj48S1q8OPYKw7mRK/JjXIicnS+u1NNQhcaPB5/4qeVFXIjR/BgHJl6fM3HfiCCpclfyq60f4cvd3+ADze9+QwIEHPvDpa8xOJts5+nh5yl3l7Epso5d5dtp8Tddsq7NTwMeXaOpMkoql8eSNpqi4NY1Wqodu8d0Mt1C/SxYxqJc3W/Fe15UiKiKoKmujHzBJJnOsaSlktFYionJDHffshZVnbFr3LFqGbevWsZkJstLHd2Yts2/vLAfW0qWVJbx7k3rirkqreVRJrM5akJBJrM5OsZiFEwLRTiJUkf7hxBCFBPJ5sO0MNlUXcs/3nYPz3Z38OnX99MVn+C/7biRiMc9ZYwUjOcnsQI2Tb46NKFiSxtTmpxInKMt0MjSQDMKTuX3a8rWM5qPkTIzbIg4WZVrQstp8NbQny31kKhCpdnXyCODjxe9JwoKPs172dwX8yFtZUomqyIUEkaCF0b3vum2AfJ2nrx9IYeHik+9eIyEf4HzQ7kRvt7zbX6l9UOE9PltMbNXwmzBJCqq+e2lnyKkhYrnU7kCuqaQK5gEPO4SqoL5YM0yOi+EKnclv7Ps1/lsx5c5lTzDE8PP8vzoXlYGl3Nr9W7Whlehi7m8KFJK0skcvoCnJPhMSkkyniUYvnSVvsXiVO8wHpfORDKDS9eoigTwuXWC3rmxMRcibmTIW4U5Gb4/DVz0iUIINE3hdPsYK5fW4NJVGmsjDI0msG3J7PmdzBfwaCpPnD6Padvcv34Vt65YOnXWCSE2pkLNQx43016QoNvFhvpaRlIpoj4vz5/rZHVtFccHhudQ9C3UR4+msadtGdtq6/nSsdf505ee5o+vvYkaXyUpM01fdpCuTCU1ngrCUx/3axPHiRsJwnqQGo/DzzCaj6EIhVPJ8zR4a+jJDBZXuRZ/A+ICISKEoNXfzG8s+QTf7P0udZ5q1kfW0uxrnMMqfrlImWn+z5l/YKwwQ2+3LbqZDzU/RMKIoSsuslaapDmJS3FT52llKNdDmauKvJ3DlhYhvWzB8Ggbmy91fYPXJg6XHL+x8joeqL/3Dfc7Hc8w2Z0guCTAQPsw3oCHsppIifCYzORI5woMx5Nk8gYVIT+jjLO8rhLLtnnxdCfblzZysKOf7Usbifgvnje1GE3E0Xwq+M2ln+CzHV/mSPw4ebvAkfhxTiROsza8igfq76XN31K0j5mGE/v02t5zbL1+OdKW+Pxu0uk8ZsHi1RfOsO2G5RTyJtV189vkFovReJqJVJaA12YyncW0JIlMjs6hGO+9cSP6RRIBBYI767ZdsQjUy8Ulv3TDtDh+eoC7b13HmfYhPB4dt1vnyRdOccdNq53i01Ly+VcOMpxMEc/mKPf7OPPUPAWlpGNJN22LD27bRH0kjEvTqAr6ifq86KrCPWtXEvK4uWFpy6Jr0YLzkVT4/Pzetl0cHR2iYJkOw1eyE8M2WBZo4eH+J/jVtodQhcrBiWNsL9tY0kaFK4pP9fC17iHCepBVwaUsCTTx8tghOtO91HmnaARnGZWFELT5m/mDFb9dFBwLfUzJZBaBKK5oiXiWyupQUaMrDpOUnEmeL3EPuhSdW6tvJKKHMew0WStFwU4R1SNoio5P85C1JpgwTFyKB5dwMZLvZGlg/Rxj23QdnLMXeBe8qpebq24grIfe8IRIjKc4te8cDW11jPaNU8gWWHvdSnpPDxCtDlPZWI7frdMzNjFFm+jFtCzcuuaIO+lso4UQIFlU6LmxSFIiIQQRPcwn2z7KZzq+yNH4CcAph3F48hjtqU4eanyAGyuvRSDIZgqk4lmO7O+goaUCf9CDx+vCKJh0nB5Ed6l0nBqkoa3yEk92MF02wpIWtbNiRMCZZ/mCia6prG6qIZnNk8zm2bN15SU1MQG0+mt+ZluyiwoRKSWvH+9lw+oGgn53MUVh28ZmvvDNl/nG9w7wjj0bCPjc/MrOrUV37f6uXgYTSbY3N9JUFr6gzak09FluWSEcJi6gSCK8+LiMUqiKwqZqJxLRsE3OJrvYGFlNSAvwUOPduBUXT4+8zLUVW+hI9cy5/1SinUZfLc2+Bl4Ye5WgHmBb2XoSRorB3Cg5K88zI6+wtWxdUasRQqCLSwfvHDrQSXVNmPIpt+szT53ggfdcM0eIOAbVV0oMqssDS1kaaMOUBhOFYbxakIKdRUEhrJajCQ1dceNV/OTtLEFXhLSVWnBb9fL4/hLaRoD14TU0+i7uzbkYpJRYpkVyIs1LD+8jXBnm9Kvn8QY8JGIpmlY6rGqmJRmaTKEqClUhPwc6+qgvCyMl9E8kqIkESWTz1ESDDE+m8Oh68fuYD7NjaDyKmyWB1gWvFUIQ1kP8SuuH+Nuz/0JXZuYbSJopvtb9bSrd5awJrSIQ8nL+ZD+ty2tIJXPUNpZjS4lRcOKTOk4PsuuW1aQSWSqqQmj6xblHziTP8fnOr2JJi19p/TCrQyuKY11XHkLXVLpHJkhm83im7CEXe+/Z77RYTGf4guPivhKC56IiLpnOUx7xs3JpNee7Rtl7oB2XruHSNT784A6a62fU5ZDHTdjrIerzsqGhloqAn67YBEG3m5DHU/wT9nqI+Lwl3BZvBaSUdKR7WBNeRlB3IkPrvdWcSJyjzlPFmtDyOfdkrCyTRoK14RX4NS/31N5M2szQnRlgvDBJf3aIjJllRbDtkkQ488G2Hc+QqiooqnDIlZRSDhGH3HmAU8mzxWOqULi56gZ0oaMJHY/qx7QL+FRHY0gYE0waY4znB9EVNwEtQtpMENHnZ8+PGwn2ju0vOeZSdG6puvFN2XLG+mN0HOkmPpZg0y3rWHutM0kOPH6E5lX1DPeMFd9VUxQsy2Y8laEi6EcRjpvzZN+wUwExliBXMOganSBvXNzmYdgGAkGbv4XfWfYp7qq9/aLXCyEod5XxvqYH5xQ9z9k5jsZPOgKtawyXW0d3O4bXFx47hlEwiZQFME2L2qZyCnmT2oYyVG3+cZtOeNsXO8A/nP8MA7khhvOj/NP5f2N/7GDRPX2ie5i9J7o42jnIoXN9PH7oDI8dPMNTr5+7JInWpVAsFWFbxApJPtv+E/753A/n0EpmM3lSiWwJn8xicNGZHPS7CfodVa21sZxrty1h9XKHt9HndXHDjmVz7lGEoC4U5L61K+e1FVu2jTFFUfdWYtoDszq0jBPxc0gkvZlBKlxRmnx1c66X0jm/vWwDR+OOR8SnevGqHvyaD1WoxY+1wVdzWX2RUhKfzBCN+jEKJomEk7G7YlUd7edHaGgsIxCcEUp7x/aTnVWbptnXxLrIGoQQ5K0sCSM2VfbRCZZyVtcKqjyNRFwVZMwUBTtHSC+bty/7YwcZzZduN1cGl7MsuORNrUx9ZwcJVQRZurEFy7Q59uIpVlyzhNHecZ771sv4Qj6aVzUAEpeu4tY13LqGXjCK1Il5wyJrGGiKQrZgUDCtOUlpF0JXdO6vu4s9NbcQ0AKLegchBCuDy9gUWc++2IGZ4wjqPDUgJRXVIeqay5mMpVm+tp5VGxqREob6Yqze2Mzhfe00tlXy+ivnuWb3SlwX8I9MR/U+PvQ03+t/hJw9Q7KcMJN8rvMrxI0Et1TdyKqmKiZSWTRVYfvKJl5v72fXqha8bh11ni3d7AzxS+F4vIuDsbNMFtJ0pYdoTw2StwxurFrPxuiS4nWDvTGGemOs3tzM5FiKxrbKErf/QrikYbV4oaaybWPLJRu0peRQ7wDLKsuLW5NpSCk50j/EmeFR3rtlbk1VKSV5y8KtLr6U4ELQhMaygNNfGxuJpNFXi0CQtrJMFOJMFOIlP8KSQPOU90aClIRdpR4GiXzDFdUURaGppYIffe819tyzAZdLx+t1vAGuWTkqE8Yk+2OzS0wo3Fq9G68yvc3TWB7cRN7OMl4YwrDzqGgYUx+oU6xaIaCFyZhJXC5PyTumrQzPjrxY4g7UhMZt1Te96TKMG29aw/nXO0nGUoz2jpGIpSiriVJeJ/EFvVxz5yYQTpJbW1UZtZEQQkDfeBxdU1EVhV0rmgl53Vj2VHq7dEifL4Z7au/Ap3rnJXy6GFShcnvNTbw+eaTopVoXXs22ss0oqoLX70ZKycbtbbg9etHg2jhlA9m0cwn+oIdrb1szp20pJRkry3f6vs8zIy/MG8uStXL8e89/MGkkeEfdXexa3UzBcEiRblq/BE1T57ULOuUz4yhCocx16Szr1eFmlgcbsKSFYVsM5mL8j2NfYSQ3WWzPtpycJlV1Emj9QQ+XlN5TuOLqgGXbPH2mnfpwqESIOPQABT794n6iPi8PblpbpAeYPv9yfw+9iTgPTREqzwdbyiJT1sUwOzJVEyqqUIsfmSoUzqW66Ez3cU/dLcXrpyeRS9HxqHMJhFShEngDNWOEEITCXpKJLI3N5RiGhWHYvPT8aW68aVVxBZNSzsm3qPfWsiW6ofgBK2ioioYuXAR9pR6BlcGp4k6qs3VSlNJgKiklB2KHSljXAJYHl7AmtPKK7I9H+2Kk4xmGu0cZ6hyhaWU9o33j3PjunWRTTqRyykqS0IZJpKbC3qeCbydSgIBY7oJG3xiZ+6Jg2CYV7nL6s4OUuaJcW7GDnkzv3AsXqny6wHFTWjwx9AyvTx69aPyGIU0eGXiMhJHgfU0P4nctTCA+jYlCiv95/OsIAStD0zWonWDQ6QRSC4k9JTSuq1zLrorVTBeQ82luqj2R4oJYyBk8/5OjtK6sJRT10XlmEKEoVNTMzW+bD1dciGiKwjvWrypRwaSUZAyDf3x+H2V+L39w6w0ljGXTAuQ/P/Mo7121fr5mAadsxL8dPsANjS1sqFq8NfrOmt0lNgyv6mF35XZ2lG2c17axKbpm3jDoFcG2i6qPBTuHlBLXFONWypjEwiSkOcFXgaCHm25dU4z+tS2bsooZQpq0lSkpmC0Q3Fx1A351hjfl2NFewmEvXZ2jXHfDCjRNxbYlIyNxjILF2FiSysoQqWSOFatKSwakrQxPDj9bok1pQuOO6ltKwujfKAo5g3Q8zS3vv44ffeZJqpsr8QY85FI5kpNpTu8/xzV7NiGRfK//Ec4kz82ZXoJpFf1Kexok9gKTedouETfifK7jK1fsedPax6W2HBLJC2MvkzRTfKzlA5dkkdMUlaDuZTQf52S8e6oNGM/HKXOFitqLxCn47VFdXFO+YkFNUwK5nEHrcmeb3nV2iGy2sOj5dcWFiBBO7drpgZNS0h2b5Iv7D9EcjfA7u3fh1bUSTeHgUD//+ZlHGUqnFmw3Z5r87YG9fP7oazzReZ5rG5oWvPZS8Osu1lZWc01tPcyz5VuowNTFk5ugL3OOg7EneUfDb6Dj5tDEM3Smj/Pepv+CW/UVtYmuzjFefO4UFRXBEi3k0MSREi2hzlvLjvJtJT9mNpOnra2Szo5RZiaapLIyRDKZpbtrjPUbImQyhalYnplx3jd+YE4m64rgUtaFV18RLWSsP8amW9Yx2DnCym1LWb1zOVKCL+TlwKOvE4gGkEiieoRfbfso/9r+efqzg9R6amjw1VHlriSsB3EprsvemlwMUkoOTBxi3/iBOWJkWWAJOSvHcH6EBxvup9JdMW8bPw1IG3JWDvSLawBBzcttNZtLKtxJKXm47yXuq99Z/E5tafPd3pd4d+P1F88NkuD1uRgdmkRKyOcNvD73gtQCF+Its26a0qZ/MsHezm76JxO8Z9NaVlRVkszncWtqiQvXlpRUj7sQUkpe6uvmy8dfx7Rtjo8Nc3xseMHrFwNNUdhWU8+f33ArSyJlV2QSSSQ+NYAu3NhY9GROs63sdlzKTLCUEIKW1gp8vg2UV8wkmmWtXImWIBDcVr2b4AXlG6VkDi9IKpUnly0wNpZiaCjO0NAkmXSBhsYyvF5Hw0iYSR4feqZEC9GFzl21ty+ohdi2JJMv4PcsLmW8rq0aBASm3mu63MKuB66ZxdDvtFPhKuN3ln2KvJ0noodLgvOuZLyDLW32ju3jyOTxOQJkTWgVv9b2UTrSXfzz+c8ylBthT82tl0hiW9wzXxjdS6u/ZR4qx+lrJKMTKQzTom90kvrKCLmCQU15xaK26kI4dIeuosCQU1nJavGYJW1UoaApC9sYpZS0nxpg6/XLOX+in+qGMrx+N+PDCeKxEJHyudSNF+KKkRK1j8XImSZhj4fhZIrBviRVQT+7WpuoDQVRhOCHx0/zH68f53/fexuN0UhxQK6pred/33Ab//XZxxZ8xrUNTXxg9Qa+fuIIG6pr8KhvvOvnJ2L0pxK8MtDLf332MT675x2Ue31kTIOcaeJWVSwpyVsmlV5nKzGQSuJWnapyk7kclT4fqlDIWWnixhhVnqaphC5nBY0bY9jSYklgfXGMpo9b0qS6pqZEGzs0cZiu9EzMQr23lh1lW+f8+IWCibtoiJXk8yahkJdg0EN39zhr1taTyxosXVZd0v5zIy8xeEHq/frI6ovaQjIFg//18LP8/r3XUxm6NBXltHA70j3I4c4BfvXW7SiKYO/pLobjKd6zc2arKoSYCo1/6+gXTdvkmZEX+Fbv9+aQHm2MrOMTrR8moocJ6gHWhlezd2wfmyLr2RSZa/RfLKSU9GT6+Gbvw+iKxkON72JH2VZUUTqRBeB2aeQKJt3DE1RGAkQDV66iwWJhmjZGwSQU9VPTWEZ5dZhUPEso4it6zC6FKyNEgGfOdnCodwBVEWQKBpqqUB0MsKG+lmvbmtBVlWP9Q/zPu2+hIVKqrgkhuKVlCZ/YsLUYCHPheY+q8Ttbd3F8bJj/c9Me6gNvvBzlwaF+PvzId8lbJucnxnmxr5t3LFtFTyLORC7LirIKUoUCx0aHuHvJChKFPIdHBmkIhskYBYbSKcJuD9c3NJMwx3li6Ku8t/m/luzlzyePoCsuutInp8bIZiDbQcZM4FI97K56CJdwjLdpK8OjQ08VtQQFhdurby4pIi2lZGIiTTjim7KD2OTzJufPDrF+YxPDQ3EEsHxFLfteOU8mU2Dlqlo0TWUoNzKnSpxX9XBP7Z6LhucL4TzXKX85Q9Y0bdheiA7CsiURvxfDsnAJlVzBxDsVXJg3TFzzcJRcSUgpKdgFfjT4OI8MPIYhZyyzAsH2sq18pOV9BKfcwTo699XdxZnkOb7V+zDNvsY59IWLRc7O852+7xcD+T7X8RU6Ul28s/6ekgRDw7LJG05UdVnQh0tT6R9LEAl60RbIF3sroGkKG3Y4rv2GVidD2DfllVp0G1eiIwL4+I4tfHzHFiSS86PjuDQNw7J4vW+Av3lmLxUBHx/YtpGWsvlzDBQhqPYHGEgl5j6AGSoAt6qhCOWSlHEXQ6XPj64qLItW8afX38K6ipnSlkGXi3KvD11R0afUwLFsBk1RKFgW8XwOw7Yo83rpTkwS9qp4VT+acDFtQjNknt7MadoC61ga3IRAkLczvDz2CA82/i4BLVJiM3pl/NUSj0Czv5Ed5aVaiGlaxCczrFnrVNCrrYty+PVuXLpKPJ4hlzPYtLkFRRFsu6aNvS+dZXwsSGVNkB8PPs6EMVkyBjvLr2FJoHXe3yJvmAxOJDEsi2zBYCAWJ286f5dSkswWqAr7KZgWR7uHeP/1G9FVhc6RCcaSaTqGxxmcSPLV5w+xZUkD7cMxVEXw/MkOXjjZyX+57wZ87itPGDw9nikzzTd7v8sLoy+XbN9UobK78jre2/gA3lmUi0IIlgRauK5iJ08OP8u3eh/mE20fxiUur4+2tHli6BmOTp4oHjOkwRPDz9CT6eVDze+lyddQ7Kdb19h3optrVjVx+Fw/69pqKRjWHCEyHWV6pSJMJTNETiXa0QJ/vxSujBARAk2deaiuqoTcbiqDflZUVfCO9at57lwnn335AL95/Xbqwm88N+NKIG9ZBHQX//vG21lXOaP221Iykk7T454kbRhFu6VbVckYBgUrSVMoTKDgJmMYNAbDSJGf9TE61/dmzrA0uBFw4jZcipuMmUQVKl41UGI0jBUmeHTwyaLnQBUqd9XcNieTVtNUWlpnuCRWrS4NmAuHZ1yDLpfG7ptWAXAsfpKXx18tubbMFeWu2tsvGp06bVRTFQVVVegeHKNrdIKmigiWLdm+rJFULs/D+48X72ksD1NXFiJvmKiKwrt2rAXg/OAY5SE/zZVR/O7+kpSHKwknJ2iIL3Z9g1OJ0yU2EJeic2/tndxTd8e82bqqULm79naOTB5jX+wgjb6GBYuNLfTs1yeP8qPBR+fEEkkkp5Jn+ef2z/KJ1g+zLLAEy7LJ5ApF6gzTsnDp6ry5Qpa0+UrXUywP1hPQnC1PV2qYgmUynk8Un9+fGefIZEeJYXUgO86RiY6S9x3Nx+nPjuGdJ4zhjeAtzxsWQjjEzauW0Voe5asHDvOp67bPCUT7aWIyl+XahmbWVFSVDG5jMExvIk65x4dhJVlfWVOisuuKQrJQYDSTJuR2E3C5SJow4yURGHaBpDHBmvAuzqcOM1EYpsrdyGi+j6irGm1Wjo0tbR4deorh/EzJQ7fiImGm6Ex3U+mumKEVEKWuwsUY31Jmmu/2/5D8rEhJgeDOmlupcVct2IZb12itLiOZzRPwuGgoC9Mfcwpvq4qCrgr8Hhe2lLg1DUVMjY+mokknY3syneVM/xghn5uheJJ1zQ5rvqoqi41huixY0ubI5DG+1v2tkvEECGh+3tv4Lq6v2HXROsLlrjLur7+bz3d+lR8M/JhqTxXbopsuOdZSSjrT3Xy569/JWqW2FwWFJl8D11XsYF14DZVuJxVBUxVGJlJsX91MwOsi4PVQGZk/2rY7Pcwj/fvIWYUp963AlBYqSkkwWsE2eXr49Vk+OycO5tnhw6X9BVr91awOXRk2/7dEiNhSYl5g2xBCsKKqgvvXreLps+28Y/3qy8rSvZLw6y7etWJNyfMt2+ZsbIyIx4NH06j2B/n3U0e4Z4nDKRJ2u4tFxKMeL2srquhNxIlcEHsmhGBl6Bo0oVPprqc9dZQqdyM9mdO0+EsjG01pEtZDrA+vYSA7yKSRIGNl+Wr3N3EpOmE9RJ3n/2fvveMjSatz/+9boXO3uls5SyONJuecdmfD7OzORhY2ACYaDMYGgwO+YDD4/gjG9vW9NjiBMWBMhl0257w7u7OTc1bOuXOsen9/VKsljaSRJqy9vtznM/qM1F1V/VZ11XnPe85znlNOg6eehpycX4Huyxf7XezmNqXJk73PcT7aMun1+Z4Gri3eOidPMGuYlieiCNJZy82eaa9kJsvJzn7O9QxypmcQl12ntsTPSDTBnjMd3L5mMbqmks4YJHOxgKuxpBljhj7R+wxP9Dw7JYBa5ijhg3XvZYlv4axehRCCjcF17B85xP6RQ/x760/w6z7me2YuB7D6yvTznZYfMJQenvLZt5XfzPrgmkmK9RYpDBbVlWLTVKSEjUtqZ4wzBe0+vrbiQ6hCtZY0CPYNn6HaVUyZM5A/5lO9+7ihdFVe3cyUkqd69nFz+bpJhltBodDuw/V29kRcuj5t+0shBAtLixmOJ+iPRCnz/ec0RroQy4pL8+MZQzidotpXQInLCn65dZ1bGxZYaVtNp9Ljoy08SlOwjBOD/Rzu76W2wA9YGp+jmX664mdRhYZdsW4Yv17CUKqb4XQvA8kONhbumvSZNsXGbeU72VW+g1g2Tm+yj/PRVk5HztIab2ckPcpAaojDoWMoKLg1F6WOYua565jvaaDWXUOhLZjvkTsxG3M6co6ne5+bEky9p/pOXOrcsgCRZAqfyxLESWWy2PWZA6LpbJY3z3VwzaJ6ygNeukcieB12dp9qY/38ar79zB4+desWYqk053utmFlTxeVzMsYettZ4Oz9p/yUnwqenMEN1ofGhut+6JDauTdG5p+oumqOtjGRG+Xbz9/mD+R+nyjm1ullKyXB6hH85//1JAtB2xc7Woo3cUXELhbZg3jj0xiM4NR2fbqcvGcWt2wglkpQ43Xjs1gMdz2Y4OdJPuduHYZpkpYld1chmbESMDGuLrUrotngfla4iqnM8EVNKgjYvta4SbLl2EIY0Cdi81LpLLmpAx5IZY2LVGTOb55VcNZ6IlJKOMz2UVBfmGypd+P7ED6z0+6Z9fez3dbVVZLLTC8mMeTFimuCwYZpIaf2fNa9CL90Jn+G12ZASMqZVZ2NKSYHNang91lB8fsByRctqx7uLNUeP0BE/zdHRVwnaygllxgvbVKFR517K4z3fpcRejVcLTBmCEAIVFZ/uxad7me9pYGeZVT3clejhVOQMJ8KnaI93Es3GiESjnIu28EzfizhUByX2Iho89SzyNjHPU0fQFiSejfOj9p8TM+LjnwPcUHItC7zz53RjSClpHxihodQ653gqTcDjmvF78zrsfOSGdRxr72P3mXYaywo52zNIfyjK79y4nj1nO7DrGiUFHn722hHetWnm0oa5jC1hJHm+/2Ue63mKcHZ67rkilHwGZq4QQlDlrOAdlbfxg7af0JPs45/Pf4/fb/wdyhwlkwx1KBPm280/4EzU0mURWAHad1bewZKCRVO6AhhS8vNzR3hf0yoeaT3JLTULeKWnhWsq6vHolhFJGln2DXTy7oKVtEdHOTM6wI1V8+nIjNITD7O2uOpyL9uMOB/p5vBoM++s3oYAft25mwW+KmpcJfh096xlJnP2RMLDMR751xf47b94F6lEhmgoll8O9HUMEw3F2HLrakwx1mxY8ubQeZYWVOPW7GRMI5/tsKnqjLKHD545wb6e6fuDmEhODg7wR88/gf0tSIOZgJmz/qmsQdLI8L4lK3n/0pnXxXbFxfWl97Hcfw0d8al9SEodVjxksW8jc6FyC2F1iPXqHhbq81ngbeS28p0Mp0c4F23mcOhYXrAoYSRoi3fQFu/ghf5XcKlOKp0VaIpKS6xt0nHr3XXcWr5zzkQqCRzv6OeW1Za2ZziRoqbIz1AkPrkeh/GJQgFqi/34nHbWzKtkOBrn3i0rsGsq1yyuR0q4d/NyfvTywTk1nJoyJmkVQJ6JnOOXnQ9zJnJuUhBTwBRC2dh+YzClSX8qRJlj5hSuEIJtxZs4GTnN60N7aY2388/N/8bvNXwkz2gNZ8N8p+UHHAtbKXy/XsDNZTdwXck1uNWp9S8DyRhOVePueUtpi4yiCkGVpwCfzTIeKSOLTbGWixZBTEHLLZ81xfrbqU0NCF8NjHkigpzsYzaBQ7GxZ+gUhjS5pXzdRfef1YhIKRnpD1O7qCLXTS2dX9SpORGWgc4hOs72suXW1ZwIdRLNJHFpNhJGmrORHhQhyEqTFf7aWaX528Mh2sOhi26zr/fymhBdDn5x6hjvXLAEj2369WOFs4EKZ0PeZR27Xa0vY5jXBh/mxtL3cmj0RSSS5f5t6GJ2zcwxCCHQhEaJo5hiexEbC9cRzcZoibVxaPQox0In6U8NkJVZYkY8PytOhILCtqJN2BXbtN7hhZBS0h+KoiiWhxFPZ2jpG2b7knkMhmP0jEY43T1APJW24hsT9u0djfC+a1YzGIlRXejHpqnsPd/J4qoSFCF4/XQb79m2kqI5kNcmjgegLzXA4z1Ps3twD4kLYh+FtiDXlWzjlYHdkwKr0WyC46FW4kaKVYFGstLg6GgzhaUr0Zl5eaYLnfuq76Y93klXoodz0Wb+8fx3+d2G38au2PhOyw84NHoUm2JjXWA1d1beQoWjfMbjqULwwzMHuK12Ee3RUeb7i1AVQb0vyJPtp6nzBdlZ3YQQgrSRZTAZYzgVJ5xOMZiMMZpKcBl296KIZBL0p0boTQ4zko7wfN8h1hU2WcxXRWNb8TL2DJ1iJB0lYJvZo5uTJ/LMj18jWObnQ196F/Zc+bq/2Jf/cguKvQz3h0FA0siQlSY9iVF8utMqPU6MUO+5+LpsDOvLq1hXXjntnJ2VksfPn2Z7zTx8tqlBOQmkzAyOXO2LyeS5f6bHRmLZxbGlVNY0SBvWjyklg4n4jEZk8qxsjh2NvlQ7x0Z3sz54M6WOWiqcDTzc9U8cGX2ZFf5rWehbj1u7NMLcmJfi070sL1jC8oIlxI0ErbF2DoweYs/Q/il8EOs6mPy041e8Mvg6K/xLWelfRpWzckosJb+9lLx5toNdqxdytL2XH7y4n6DHSbnfy/GOPgpcDkoLPEST6fELCEQSKR7ae4JP7dqCpip874V9fOa2rZim5D9ePsjHdmygqaKIv37oJb54zw34ZsnQjd1fo5kQLw68yvP9L0/pKBfQ/Wwr3sT1Jdfg03zsGdo36f2UmcE0YTQdoycxjEQSysTpig9S5y5lJgghKLIV8r7a+/nm2X8hZsQ5Gz3PN8/+C3bVxpnIeeZ7Grir8laW+hahzVJXFbS7uL9xBUeGeil0uBhNJTg9MshQMo6uqOysbsqle00KbA7KXT7ShsFQMk65y8dIKkFvPJK/LmkjS3diKF88KLGqe9vjA5MCqyPpCO3xAUodAZwXNPN2qDpFtgJC6ThuzcmKwDxaon0MpcKcj3YTzSawKRod8QECtpnp73OOifiCHtKJNKHBMC6fEyS0nuikYcXkNFHWNAjaPWg5zr6CoMjhY4GvYk51mVuqavjUmk3TWr1kNsuJwX5+Z+U6qrzjcZdINk5fcgS/7uFIqJkaVwnzPBVEswm64gMU2QsYSodRhcp8z9zk/y5kaM4FEisOciZygKzMsLX4TuyK5doW2sq5p/oPeWXgQeyKc1Kq93Iwdg5uzcVi3wLcmotDo0dnLJtPmWmaY600x1p5rOdpKp3leYp3latiEneiezjM0poyKgI+ygM+fC47tcUB7LpGVWEBXocdv9uJ06bzjvVL8vKOJzr7qSsO4NA1in0eqov8mBKWVJcST1sDayovYnltOVnj4rosUkpGMyF2D+3huf6X6U/25z0egaDYXsTWoo1sK9pEkb0IRQjSFyjXj20bysbISoNoNoFbc1Bk91HvmV1YSgjBEt9C3ll1Jz9u/wVZmaUlbi0T1wfX8OG635pzmwtDSiKZNDZVZVVRBSdG+gk6nBwY7OLainn5eyxlGMSyGdqjo3THwgwmY7RHR+mNR/L35Fgq99DI+XzKV2AtSx7uen3S52ZNg0e79/CB+h3cU33NpLHqikaBTcOl2clKg+FUhFp3CSUOP/XuMqrdJbTH+pnnmdnDgjkakbGaiI5zvbz60D5ufPcWTNPk1//yHB/687snbZs0M1RpTlJGGjP3BEYyiYsWAV0pMqZBf2oUTagkjBQnwm0U2/1kpUGx3Z+zqDo9iSHmeyrndMw8gWwO2459uUGjks3BamyaPZ+hmXg8j+bn5vIPzkmNaq6QUnImep7vNH+f3gv63yzwzsetujgdOTspyJo207TE2miJtfFYz9PUuqpZF1zFKv9yShzFVAR9+WI5AaysGye2ragdlxew6xrrGqvzf6+fX836+dUWZ0RVuHfzcgSgqzauWzLe9vO916ya8QqY0mQwNcRrQ3t4ZWA3/anBfNzFptiod9eyrWgTq/zL5ywoXaC5iRCn3FnIaHrmSvHpoAiF60u20Z/q56ne5/NjOTJ6jKf6nmdX2Q6cqmPWcSSyGX505iAfXLgGIQQ1Hj/fPbmXoMNJQ8F4n56hZJwVheU0+IIoQDSTorGgkKSRoTNqLfPTpqVKdk3xMlRFtSbsXPp3IqtVIDCkiSFN/Db3lAkxlk1yPNTK64MnSZkZAjYPBbobt+Ygkk3wfN8hDgyf5TML774oMW1uRiQ3KEVR8PjdNCyrxjBM/EVenO7JLmnGNCi0e/DoDk6FughnEvh0F8pb1G5JCEE4E8s371EQ1LjL8ekuTkc6cKr2nAsbo9Y99/6us0FKyem95/H43Qz1jFBaW8yexw9w7bs2Yi925rfpbR3AyBgIRWAaJoqi0N3ch6arrLxuyRWNx5AG+4YP8sO2n01axggE64Nr+EDdu3GrbnqTfewZ3scbQ/voTfZNCkamzBRnouc4Ez3HQ92Ps9DbxJaiDZZ3M02j7ouNV7nAaE635XSvSynJyCzt8Q5eGdjNvpFDjGasB0ZBocRezEr/UjYUrqPOVYNNmXuAUSJJmpY2hk9zoguV5mjP7DsyPjnois47K+9kNB3OSykmzRS/7nqUc9Hz3Fd9N7Wu6osu1z26DZem41B1UkaWY8O9KEIwnEywf6CT+QVF+Gx2emJhNpbVIITAkBIjlxrujoXznphHc+aDnRPFp6KxFKoqcDpsk8YvhCCdydLTO0pFmT8/puF0hBOhdlYHGulLjlLssN4rtft5umc/Ls3O7zXdgWMWrZlL5IlIpGkpepvGVDHXtJnFpdpwqDYMadKTGKE9PsiuitWEMwm8uvMtIZh5dRdZaaAIBQnYFZ2kkcap2ulPjlDpLObwyGHuq7n+ohoJ1vmYkEvNzRaEPH+4jcWbmggNRrC7bKQTGUKDEZqPtqPpGsu2LaS4KsjJPefwBtwcevEEN7xnK20nO6lbcvmpurEU5+O9T/N4zzOTGKm60NlReh3vqLwtP0NWusq523k7O0uv50j4BC/1v8qZ6LkpQr3RbIx9Iwc5MHKYMkcpGwvXsqlwvZXa5Oq2nLSC0JKR9ChHQsfZPfQm56MtpMwUqlApthexyLuAtcGVzPc05CURLnUMft3D0oLKPAeiJdaLV59Znc6qUzEYTA9zMnwageCa4i04VQcfqHs3STNpLRuxDNTR0AnaYh3cVHY9N5Rce9GU8k3V8xlOxTkzOkCx080nlm5iJJXgoZbjRNJp1pZUsihYgjeX7i1yulmulKMKhRPD/dxau3DSNTAMk6dfPsmyBRU4nTonz/Zit2uUFHpRFIWqcj/Hz/RQ4HViGCYHjrazelkNQkA8kWFhYynvr9/BmUgnfalRwIqjmEjOR7v52ooP49OsZ0u7SBB6zjERsJS4eloH2P/8caQpCQ1GJxmSpJGh0VvG+WgfGTPL9lJrpj043EJbbIDtpUtQrnLjZCklGTNr1b0kR8hKg6w0yORUpbLSYDgdZnPRUp7vP8D24pV49OnJVpIUo+FvUuD9HVRRAGSIJ5/Hab8GRZl64ymawul95wHoPtfH/DX1BEoLSMZT41KAI3EKy/30dwwRC8VxuO2s27kCzXZ5rRvHSs1/0vFLjoVOTiJYFeg+7q26i61FmyaVnguLM49H97ApuI61gVWcizbzfP9LHBo9NkkUGqxAbHeyhwe6HuGZvhdYHVjJDSXXUOeumcJ9uJzxhzJhzkTPsW/4IMfDpwhnwuiKjVJHMYu8C1jhX0K9u86KN1yh8VKFMqkr3NKCumnHlJVZ+pIDHA+f5ODoEVpibaSMNCv9y9hUuA67aserefho/Qf4dvP3ORwarxkKZyP8qvNh3hzez63lO1kTWIlDmZyBE0KwvLAcU0oW+ovzrxU53fz2ovEUqlcf389vc+C3WZ7+p5ZvmeLBKYpgWa4NRyicJJM1UBTBwHCUhQ1W0PhcSz9rV9Ry+EQn61fVcbaln8KAm9IiK6aYP6a0nID9w2dIZFPcWLaaZ3sPcEfVZk6EWlkdmCrKPoa5GZGcrkBhuZ87P3YD81fWgoTy+hL8xeOsU5/uxKc7KaUgf5EAriudKmQ742ddRhrLp7u5sWwN4UyMMkcQicSu6NS6Sil1BNCFhioUatwlM6qWjcE0QyhizMjoGEYf8eQzuJ13IEkhsDGmGYIERbXqWooqg3Sf76V6QQWmYWJzjFHTYd8zR7jlQ9dx6s3zFp9CVUgn0timIe7NfF0sevcL/S/zeO8zhDLj1c4CwQLvfH6r9l7qXDUXPaYQApvQWeRtYoG3kdZYB8/0Pc++kUNTjAlYvVheGniVN4f3sSawkl1lN1Htqrxs5TETk593PsgbQ/vy5LrFvgU0eRspc5TkWzi8VfGziWSxtJmhJ9nL0dAJDo0epT3eQdyw4mcLvQvYUbqdJb6FedEmIayeNR9r+BDfb/0xe4cP5I24xDLu327+Hg3ueewsu54V/qU4lPF4iRBiSj8lYb1x0bHmt2My52VoJEZPf4iigIfDJzopCnpQFYVzLf2sXDIeq8pmTVLpLJmsQTyRpjDgQVGseMmbQ6d4Y+gUjZ4KOuMD1LpLKXcEyZhZHuh8lf9z+gEKbV5WBebPGJCYkxExDWvgpTVFlNbklJcEBMsKCA/HiAzHxkvQprkgl3JDpI2ZmajJbJbRZIIX25sJOi4umHyc6SUFLoRNVdlcWYNT03KyYQLTjJLJtoBQ0bR6wCSdOUwi9Tpu5y3oWh2Q84JSGdx+l5XBKvRyas85pJSU1hYRHooy0h9m4fpG2k50Eh2N0XWuh0wqy3DPKGt3zi5+MxYvOBo6zkPdj9McbZ3kfXg1DzeX3ciO0u24piE5zYQxpuw8dy0fnfcBdsSu47Gepzk4emTaLEfCSPJqTiHslrId3FR2/ZTZdmy8F4OCws1lO1jqXcUyfwNuzYVAkJEGw6k4pY7JxzSlJGMaFhHrSjwgLG5TykzRmejm8OgxjoSO0RnvzrdysCs21gRWclPp9SzwNk7b6NuKrXj5aP37Ceh+nut/ieyE5lmGNDkTPcf58y3Uuqq5pngzq/0rCNj8V+xVSSmJZZP0Jkdo8JTj8zhIpSyejstp4+ipLpoaSikrKZgUNigKuikt8pJIZHA77RR4HRQFreXhqmAjkWyC9YUL8evu/DnaVJ17a66lOdrL2UgXhjRnXEXMyYg43DbcvqmNi7PpLC898CZP/+g1fvvL77qc6zIF/fHpo+dSSn599gTHB/v5wsvPXpXPAtAVhb/fcRs31RaSzpzAMAZJpF5BVYpQlSJ0vRJTWkuTAs9HmNjva/X1S9n/7FEKywOWtyagqCpI85F2qprK8RV68BV6QMLhl0/QsKKWyHCMioZSGpbXXjT1M+Zin46c44neZzgeOjmp05sudFb6l3Fn5a5Zg3oXw7gxqeMTDb/N8fApft31GOei56cVNo5ko/yy8yHa4h18sO4908YAjo12kzENtNyYEkaWg8MdrC+qRRMKoUyS06EoKwK6FceSkpd6z3Im1M+H5m/idKgvp9MLo+kELZFB7q9fi20WNbuxOMuk15DEs3Ha4p0cGj3K0dBxepK9k+JBNsXG8oIl3Fx2A42eBjRxcYMlhMCpOnl3zTspdZTwQNfDRLOxSdsY0qA51kpLrI2H9MdZ5FvAKv8yGjzzCNgK5lREeSESRppvnvk1te5SGr0V6LqKrqvYdJWqcj99g2GEtCqE0xmrJaeuqSiKQoHPiaoKQtEEvQNhaiqtmh6HYuOmsjW5T7CYVVKaiFx3hAZPOQ2e8osNa25G5Jb3X4PNOTVC6y/2cftHrmPzrasorrw6OqVnhodIGlmc2uRlR08swk9PHsVjs1vpR8jrdio5Elbu3xTInJS+Ka0f6/ccjVpKHjt/mhvrdmG3r0NNPoPbeQugMjT6Rbzu+0hnzmLKMD73hyadYywUZ/GmJkzDpPNMD1vvXk/HqS6iIzEKiq01ZzySoOVoO7FQgq13rSM0FOGhf3iKrXevZ/6q+mnGKkmZKU6ET/Nc/0ucCJ+e5BlYXJd57Cq/ieUFS6adLceOY5hmXrypIxSm2O3KK4xdiDFm7PKCJTR65vHiwCs81vP0pGXTGExM9gzvQxUqH6l/H/YL0n9L/FZKeGxUL/SeIZZJsSJYlc/SbS1pyI/zZKiXoVSMu2tXYlc1FvvLJ7n9m4rrUedgJGNGfNLDnJUGv+56zOo6l+yf5DGAVaC3pGARu8p20OSdP6vxmIix67WjdDvVrkp+2PazaVtNSCQjmVF2D+3h9aE3cWsuyhyl1LqqqHZVUeYoxa8X4NHc2BQ9V8A5/TLXiu9FqHGXIqVkaCRGd1+IZDJDXU0RSxZU4HFZPXuGRmJ43XYKgx6cDp0FDaX8/OH92O06q3JLnfFYZzsCFZCY5iBCKUTVavLnOeu1mMX9vMpE24vjV6eP8VpnO1/ffhP2C2adaE7HQ1MUdMVSNhszJiBmWlbmYZ2mzKs6WW0FzXyLwiqvDyEyDIe+RmHBnwEqQ6EvEfR9llT6CFmzH69rnBOTSWUY6QtRXFVoMWXjKRxuO8M9o/S09LNkUxPJeIqhnhG8AQ++oAeRa5kZHYmRjKcorirMjc0ybMPpEQ6MHObVwTdoi3dMuul1oTPfO48bS65jhX/pjDfaGNLZLD86eIR3r1yOTVP54f5D7FrYRLFnbnRzKSVt8Q5+0v5LjodPTZnhwTJovzPvA2wp3DjjWOLZNJ958xfcVLGYd9SunFTMJaVkOBWjKx6iyu3nr44+zacWX0eJY3J1d3tsBK9up8huueCZjNUVz1KzV9A01RLzHnyDbzd/b8bWEGNQUJjvncdt5TeztGAx+gyGeK4YI8b9uvsxXh7YPe1y8GJjsSk6umLDqdoJ2gJ8svFj+G1TFd+llHTEBzg0cp7bKzdyrnWAQIGLRDLDyXM9DAxFUYRAAk31JRQXetB1jXQ6y+BwFI/bzsBQhNaOIRY2lrFmRS0CiTQ6MYxOhLCBTKPa1gLTptGnvUhXTQpgLjUZs2FFSTnX1czDNo1wjMdmwzMN1f1qweoFYyJlnFT6GIriRaDBBIk86xwlQihoNo3i6sI878HpsaLohRUBCisCkHutav5kV1AIgTfowRNw56X8zkWb2TO8j6OhE4Qy4UkPrE/zsqxgMdcUb6HJ2zCtKtd0UBWFMq/VokEAm2qrLym9LoSg1lXNp+Z/nEe6n+CpvuempIQNafDKwOtsDK6bVqtVSskTXcfx6g4GUhGaI4OY0qTRV5IfS8DuJmB388vWA2wuaWAkHWcgGUUIwat951gZrGYkHafE4c0bkcMHWmmYX8ZAfxhdV5nXWErSTPJU33OzGpByRym3le9kQ+HaSUHPeDaKXXVetEBx4oQ7XitlFZz6bQW8r+Z+VhYs48GuR2mOtU5reC+EiUnSTGEicah2nBfp5CeEwK058sTNxrri/HuVZX4yWQMjxwS22awYn2laHqm1fIEFDaVsWF2PGCuTlWGy6b0gdLLpg6i2lZA5gaqvmHXsY7hqRqQ5OkA4k2BloIZQJk44k8RvGw9+dsdHqXUX4tSmGoK0YdATjdDgt3rHvlWR+Yshm20hFPs+ulaPqgYAJWdIJhqRBFmjE5vedNnpWYkknInQHGvNF9ANpocmtVl0KA7q3NWsDaxmdWC5ReueQ1BOSkkombIqQRUFv9NJPJ3BMCXJbJZYJo09pTHmkamKglObeRYWQuDSnLyr6i7KHKX8pONXRLOTY1Z9qQGSZhKPcmFrC8nhkS66YqO8o3Yl58MD1HqCPNZxlAPDHbyjZgU2RUMAZyMDPNF1nPc1bGBxwbjRfb77NI3eYkqc3knHzaQNMhmDbNbIK9/bFBsLvfOnVC+PwaU6ua5kGzeX3UhA908554FUDy2xM1xTfEs+TjNmIPK8DJnl9aHnqHE15DN0zdFTzHMvpMbdgKaorPQvY763gVcGXueZvhfoS/VPGcsYVGHFojYE17DQ10SRrRCn6pi10jqUidEZH7zoNqQu+HtCvaJN0fDb3Fh8qAI0+yaMzBl0590gk6j63LOpcBWNSGd8mK74CCsDNXTFR3m8+zC7KsasmeRHLa/z0fnbqfcUT9pPSsme7g6+svtFvnLNjZS7vVbnXDmhzDy3XFGEGC+Tzv2MvaYKkQ/GjeFSHnRVLaXA8xE0tQrIEI79By7nLiwPTiBlCtMcJZM9h67NXZNDInPNkQY4EznHifBpmmOtjKZDk3rMeDQPta5qlhcsYVnBYsqdZZflZj984hQHurrRVYWRRBK/w4EiBJFUCl1V0RUFM8eErA34+YMtmybp414IgUBTVK4p3kyxvZB/b/spnYnuSe9fyEGVUnIuMsC+wTY+NH8TZ0LWg6QrKrdWL+NbJ1/kn0+/wu8v2s5QKkZ3bJRry+YTtFuTzkSi33QwTYmVIBz/XEsjdSfHQifpSIxXeVsaH/XcX/1OFngb8wYilBkhlo1MaK2qEc6M0JvsREFgSIPzsZNsCF6HPdclUSLpT3WzIbgdNUcVOB89OZ7yZ6y8wc3NZTewsXAtrw6+wcsDr9GT7JvkmbhUF/dX382Wog3Yp8lyXQwPde7mR63Pz3n7C6EJhYW+Gj6z4G5KHP6cIdmINGMIYUfKKIrin/vxLnskE2BKiSIEXYkRWmODVjGUkSVlZpio8uDWpvLv04bBvx05wOnhQT742APYVCUvHzcekhH5tfRYIFUVAl1RsWsqLs2GW9fx2uwEnE6KXW7K3B7K3V4qPF6KXW68dnt+mTTdF6YobhTFjWGGSabewGnfiq5ZHoemVRNNPEw6fSyX3p1aljd202dllkg2Sl9ygNZYO2ej52mPdzCUHsmvlRWs6H6Jo5gGdz2LfAuY564laAtM6U9yqXj3ymXct2IpAsHR3j4WFBdi1zTODw0TcDoJupy5qmWZ81jm9lmKUFjsW8hnF/wBj/Q8wetDe4lnEzR5G3Gok3sun4sM0BIZ5L3z1uFQdSaG1jSh8MHGTXzz5AskjQy6UNla2khvaxjm4G1lswZOl43CIi+pZBbDHKfw+/UC3lV1J/9w/jukzQya0Li+ZBvvqLx9ShbJqbryGZLTkSPUuRewo/QdjGQGOTS6h63FO1lr24Y+iVdkGUwl19tZSmmR76YZpxCCgM3PbeU7ubZ4C4dHj7F7aA9no+dJGEl2ll3PdSXbLiurtqloMQt91bNvOA2klDzZs483hk7yk/YX+eT8m5DZk5ALrIJEUUqRwj9rnHEMV2xEpJQcGWlnNB3Hr7v413MvcUfVKgxpkjQmrKEnBNMmBtbe7Onk9S6raVM8kyaRHQuV5nYb+23iCeW8lLGHYaY5SxWWAFKB3UGVt4AlRSWsL69iZWk5ZW7PpI5s+WGi4XRcg2B8dtDUaor8X7E+GG3SzAMQyUTZPbSHtngH3Yke+lODxLKxnMy/ijOnQFbuKKPGVUmtu4YqZwV+vQDbLAHSS4EQloDN620dvNbWToXXy7zCAO4Jnpt+BWJOQgiCtgDvq72fnaU30J8aZJ67dpJqvInEqzu4oWJhPqMiLziG3+bkT5ftxKaouLXcvTCH+IGUkpHhGPMXlCNNSWfHEI1N49W4QghW+pexuXADu4f28M7KO9hZdsOUDJYQAh0bmqojEOiKjZPhg2wqvIHucDsNnsW4VS9pM4WYpIgvSZlJuhJt+SVHKDM8Rd39wmvm071sLdrIxsK1OXLbSZYVLLrstPx8byU7y9de1r6WqHQvw+kIN5auQhEapvBZWRkEQi2bFAecC67YiJhIXuw7RZnTT6mzgC0lTQwkw2wvXUjA5samqKTMLHdXr+HISAdL/VWUOccjzx6bja9euyMvQ6gJSxhYEYqlIs54NWn+M3PZlYxpkDIM4pkM0XSK0VSS4USCwUSMgXicwUSM0WSSoUSC3liUfb1d/PD4IQqdLrZU1nDfomWsKatEV8ap3NPR2633Zma6psw0u4fexNL68FHrqqbIXkSRPUiRrZCALYBHc1kG4yrXoFwIQ0oWFFuEwNMDA/zowGE21FTjtun5TNSVYIxXUu4so9w5tZxeFQplzsk6KWPLp4nHuDD7NqZ0JqVkJB0nYxrEjalZjqJiX36G3LB5/pTZUlM07qq8lSpnBTtKr7uozsfLA09Q5azHrjjxaCbt8XNIJDbFTkvsNG3xc2wsvB6H4szHR+Z7llLqqEAVGlJKlvnXEdCLMKWBuEhJgBACXejowoZhZqhwlM+4ZEubmRzr+upNMGOQQDgT59ML7mZJQS2QRSheFAxAIhQ/gktbRl+xEUkZGQrtHgrtHpJGmkQ2zZlwL9eWLuThzgO8u24TP2t7k/fWb+LlvtOUOnx5IyKEYFVpBatKK2b5lLljjPthSCuYGEol6YtFaQ4Nc2pwgMMDvZwdHuLXZ0/yVMtZttfU83urN7K4qOSSshcTUWgL8PmFf4iaI+i81YZiJkgpefDYCfqjMe5bsYy1VRVkTZO+iBUMdWjaFJ2US4ntXO55uTUblS7/Rbcpc/ooslvp56xp8ljnMULpRF57lNxYJ378TEMpshWys+yGKbGaCzGU7mOlfxO+CQ20q13j+rl17vl5T+TN4RcpspdRZC/j+f5HuLb4Vg6MvEqls47hdD8vDTzOdSW349Yu3rs2I9OMZEbJyCynI2dQhRXobo93UmIvxqE6aI93oCu6pcp/lavfM2aWWncpy/31udiT9bpp9IFMoSoBELP3352IKzYiTtXGb9Vv5rWBsyRzM4eZW2xYKywrSIrFKJ+VdXilGKtPULEo7T67nWpfAWvLK3OKUAZt4VGebjnHg2dO8FTLOd7s7uQTqzfwniUrcKiXHsy0ZtarI79/pVhXVckzZ8/zly+8jKYoZEwTv2OqgljWNFhQXMT9K5fPyXi+NvgYAVsxC71r0GapP7oQi/zlLPJfnPV4XXlT3kiVOL28v2EDhpTTpvtharp1IoSYGuydsj8yR+yyz1oUKqVkKN1Pk3cZHs3H/pFX0YTGULqflf6NOFQX+3KvzQaBQBUqdsVGk3c+AkHCSPLrrkf5yLwP4tcLaPTMu+g5JIzURc//YtAVldsrN46T92QWI3MCUEHYMTLHEUoJqr5szse94if6wg9ya3aCNjdZ0+Sm8qVEMklurVrByXA3C30VFNnn3iYikc0wnEhQ4fFelZldCIFd02gKFjE/UMi7Fy/nV6eP870j+/n6Gy9zaniQz2+6Fr99dpGZtwIWV8Uytpfz+UII6oIBPrJ+DdF0mt2t7bzc0sqm2urcEmfsgyxDP11bj+mQMGK8OfwssWyID9R9jlr3gksa11yM1IXxAU1RZ7w5pZQkzTi7B59gsW8dZY6LFx1OB1Ma6IoNXZmb8RcIFBQU1AlLbAVFqChCndVoWW1FQoQzYZJGkgMjh/DrBTR45tGf6qfIXkjQFpg1TjLGzo4bKc5Hu1lWUI+Ukp7EME7Nhl+/uLq9IhTc2oRJRdhRbasRwo00wwjFDfLSOilcdbegyVeGIgSnQt1UuoLYFOhLhPhF216+tPwugra5MyZfaGvhe0f389VrduDWrx7RLOBw4NKtnrsfXbGW62vn8dd7XuXXZ04QSiX5+rU3EXRMrRWSUhIaiWN36DicV195O5sxeOA/dnPtzmWUVkzlMswVQgi8djs3NTWytb6WvR1d9EdjrKosv+Qlm5SS5thxhtN9bCq8mSpX40W3lZhTYgNSSgbTPfQm2macYSWWjocqNCbYOkyZteo4JuwjkRwafZVT4f0cDe3mXVWfoNI5c4Op6cYZyowQtJXMWf3ekAY9yU7c2ihJM5GrbcrQk2zHpjhIGvGLBoclklg2RtrMYEoz193Qir8dGT3GSHqUWDaGV7/4JFugu7m1YgP/1vwkaTPLsgJrWZKVBs/1HuLu6q2XuADKYKQPoNm3IGUUM9MBQkfV5s4VuepGRBGC+d5SCu0enu89wdrCevYOtfDphTdR7Zp7fU08m+F7R/ezt6eLdz7448uOZE+HdzQt4gubr0PLBVQb/EH+9w27+N6R/fzTwTf50ivP8fXtN+HRpwa2nnv0EK89d4Jrdi5ly/WLKSrx5eUjrxSD/WEe+PfXeO6RQ3zsT3axasM8S2rgMiGEwG2zce28OsLJVL5KeSyjNZdRZ2WGvcPP4deL2Fp0GwrKjAHBhBHlmb6fsaxgE/XuxZOunV1xkJVZAnoRurBP+fCeRBuPdP8b76z6XQrtVsA2no3y0/b/w9bi22jK9TYew3XFd3NdsVWG4NIutQmapDfZyVLfmjnfjw7FQamjArfqZZ57AYpQ8OtByhzV6MJGjavhoveoKlSqXVUIBG7NRY3LkpGMZmN0JboxMUmbGTK51PRM41KEgk0RLPTVcCo8XqtT6SykNzlMV2KQKmfRJUxACopSipE+jJRJhOJF1RZf0gR21YzIWMZkDAW6E4/m4POHfsn1pYuodAXIShPFmqcuOkgpJS93tHK435KQi2WsVPHlPKoX3u5SSh44fYLbGxeyurQiF6wTuHSdj69aT7WvgP/vtRf51v43+OMNW9EvmKl2vWsd/T2j/Ov/epIH/v01brhtJbe8cy3FZQVXXH9x+M1mwqNxQiNxvvbZn3Hvh7dxx/0bL8nrkVISz2Swa1r+byEEPofltvdHo/RFY7zc3Mp7Vi7H73RwJnqIttgpHOpULzGUGaI5eoxa90KOhHZf9LMHU920xE7Qn+riHZUfpdBWnr++Xi3ASv/M7TuzZgZFKJQ4qihzWMVf0cwoqlAJ2EqodM6bdr/LgYmkzj0flzr3AOK24ltyWieCDcHrMJEsK9iCW/USzkbZUHjDrO1QLoSUkkMjR1hSsJiT4dMYMsvLA6+xuWjjJJkFKSXhTBy7qmPPxaM2FC5gMBXKv68IhbXBJh7peoOPNd6KMoeqt/x3IRSkzCIUN9KMIs1RhBqc83lcvdqZXJVsd3yErsQoXfERHKrOX626j33DLXzj+ONkpcG7ataxsahh0r6RaBLTMDFMicOhk0plKHK6+V/X3UJv9yhV5QFUoeBy2iyyGYJYPEUikaGkyJsnppmmJJnKMBpOUFzkIWuYCCWnVZlrSpUxTKtMfZp4gKoo3Na4EK/NzpdefY7lxWXsahinuAshcDh1PvSpHWQzBo//ah8/+c5LvPD4Ee7/6LVcf+sK9Iu0mrwYMmmDF588mo+Wx6JJHvv5m5RVBLhm59I5HyeRyfCV515EV60er2nDoNrv4+Mb16MKqzhLEYKRRILOUAi/00GtayEl9kqcFzxUhjT4Vec/4tOD3FX5UTza1KKwmaBdwDX4r4gxzQRVqLgv0XuJxkw0t0QKg4yZJWWmeWVwHzeXXcOrg/tYE1hqab8i8M8gID2u1p6hM9FFNBujLd7OHRW3cjJ8Go/mwW8r4Fedv+be6rux5a6hRPLvrc9YOqh2K32elSatsT66EoP5yTVhZNg7dJpYrtXDTNCExupgI+uCC6xqaeFFUe1IM4ZmW4Jp9iPlzM29phzvEq7jRbEqUIuJzEkklrAyUIOWU56ucgW4pWI5w6kYpU7fFJfixMluJDAyEmPp4krONfezfdsCotEUL58/Q730MTAYYcumGtLpLOFIkvaBYfSYSU2pl0QiTUmJj+MnugjYdAptdkQYioq8lJWMcxYmWnYAw5SoyuQbXBGC7TX1/MXWG/jWgTdYWVo+KbArhMDu0PnQH+xgoDfEm6+cobdrhG999RE6Wwb4wO/fiG67tMsqpeTcyW5OHcm5pwK2XL+Y3/njWyguu7TeNA5d57ZFC1lZUYYABmJxktksas4jKPV4KHG7aaussJY0QuBQnTgu6M8rpeR87Bjno8e4qezdFNomk7ouF1ejUHMsHqFNKEYcO27GNNEVBUNKouk0BXZ7nkY/GIlR5J1bi4eJn3Wis59vP7OHr7x7J1LLkDCSeUnOmJEgaxokjCSGNPOewoVIGSlOhE9xOnIWVahEszFOhE9ybfFWHBMye8sKlvLywGscDR1nTWAVYHVL6E4MEUrHCKVjuf40kuF0GJXJk6FDtfFS/1EaPOUzBns74gM80vU6f7ToHotwplYCAtQkYEdRZ2+nMRFXzYj4bDM3ibZuVJ2KGbgCMrdWF4ogGkvidOgYhkn/YJgliyt4ZfdZrt3ShGFKbDYNn9fJyEgMr9eBKSXVVUESiQyJZIbRUILKigA9vaNUlgcAGIkmyBomPpcDkNh1jcFwjJcON7NjzXwK3JPHLoTg2pp6opk0PzlxhM+s2zxJ30IIgdvj4Hf+5Bbamvvp6xolmzF46CdvsHhVLZu2z72BNFi1IE8+uJ9kwlq2rVg3j9//s9vxBy/thh+DTVVx6dYD5kinJ7GE8585iwJZ2kzyQt+vKHVUsTpg9SsJZYbojJ+nwbMMu3J5GayhdC+tsVMs8K6alCpOmVaHt5SRIGFYmiBJM4FEkjFT+dfAir081v0Dlvk3s7xgE4pQiWczjCQSRNJpGgJBzg0P0R+PcW1NHVJKdp9u4+UTLfzpXdvzWSrDNBmKxCn2TX+dpZQcau3mCz9+CiUX9/LpHryam5ZYJ3XuSoK6nyZvHdXOCkzMGeudDGnSHGthc+EGSh2l7Bs5wCr/CiqdFRdIPmjsKL0+L8kI4NWdfHnp+/OkSystnOaJnje5u2orMD4vHxg5z9+e+iV/sugeyhzBKWZEAv/R+hw/bH2W1mgvslROiOWMPQeXRld4a0kblwBpWurx8XiaeCKNaVoFNG3tQ1RXBjlxqpuWtkFWr6ilpy9EaYmP0VCcs+f66OkZpb6uGH+BK9cUSeJy2ggEcnKBEsKxJC8cPIff48SmqyiKIJM1ZmyipAjBrnlNNPqD08bchRBU1hRy34ev4VtfewTTsKpL97x4ik3bF17SuXe2DPLGi6cAaFxUzqe/dOdlGxCwljRjCCWSPHH6rMUVEdbNZko40tPL72/ZOO3+UkoOjr5CV6KZd9d8BodixUoS2Si/7PwH5ntW8M6q38WuzjxxzDg2I8bD3d9lXeB6ArbxYszhdD+GzHAivJeO+BkAUmaStJmiOXqc1IS+OQkjxrnoEQZS3VQ7Gym0l2FXVVRFIeBw0DwyTCSdYnOVFVvZf76LL/3sGVbWjXNVpJS8fqadZw6d4c/vvXGK9ilAS/8In//Rk3QNh6kt9udfz0qDvcOH2Vl2DWkzzfHQWQp0L68N7uf2ihsmeRZjcKoO7q68E4CRzChN3kaKbIX5+1PmRAyEECzxLZq0ryIU7BcE2FUzl3AWkzNhiwqqcWr28djIBcFeS6s3xa0VG3h//Y6rQma7KkZkzJXs6RzBH3TjdI1bUdOUpFPZvCXPZgzsTh1NU/P76rqKy2UjEk0SjaXwuO2omkJ1VZCR0TgV5X7Onu9j84YGpIREIk0imcHltFHgcxIKJ0gk0hQXeVFVhTf3NdPYUJI//mA4RjSRYv3CGlKZLMl0BkVRKC7w4HXN3MpRVRQWFZXM+L4Qgmt2LuXxX+7l3Emrl4mqXVpwzTRNHv/VXsKjcSpqgvzhX9xNWeXs61GZ04oY6zkyBsM0cegaEoin03jsdtZXV+VLCMbumU211dQUTC9805/q5MX+B1gZ2Eajd5x0pAgVU5o0eJZiUy7eAnMmKMK6+Zf5N1HrGje256NHOTT6KmuD11Nksx72aDbEnqGnWeBbzYqCLflt40aUfSMvsLloF0HbWCtMwfGBfoYScZyazobKKnRF4VzvEF/86VP0h8YlDKSUdA6F+Otfv8hAOM7K+gpuX7sI7YK6otICD+vn1/DQm8cn7XssdJq+1CBuzUXSSNGXGuRE+Bw3lW3Ltya9EBO/o4Dun/TamFCWHKvqnuPkkZ2Gz+FWHawvXMBjXXvYXLQkz0zNjx+JKU3eW3c9NlV7exgRKSXHD7ZTWOJleCDMyFAUf9BNe/MAa7fMJzwS49zJHlweO5quMtgXZuHyKopLrRs4kzEoL/PT0jaIz+ugotxPKJwgmcgwMhqnuMhLJmNg0zU0TSUcTuBw6AwORXE4dDJDUZYsriQcTqCqCuFIgvq6Yvr6wzjsOlVVQfpHo5zpHKCmJIDDphFPprFpGgioLraaTl8u3B4Ht927gW9+5WE0XWXz9Ytm32kCOluHeOnJowSLvXzmy++gvmn2BltSSpLxND/8p+dZuLyarTcuyRvpcCrFib4BGguDfO35l/Ha7YBkMBbH57AjpeVlJbNZ7li8kG31tZPiCtFsiIe6/hWn6uH6kneiMH5tRI7Sb7ugu58lJp2mN9FGlavxoqlOi0aec8onHCNpxlGFhm1CVmLiVZhUQJf7/8LixWUlpSjCqu4+OzyIKhQ8Dht+t5Ou4XGZx1Qmy98+8got/SMAfO2BF+gcCvHbN6zDaRuPs7jsOn9y57WA5FCLNUkMpIbw6z4qnKUMpUdoj3dToPu4rmQTAquplVO9uIG98PvVhMo7Km/HpV5cfHzSMYCBVAhDmpOyQkIINhYu4tneA/TneslMhARuKF1Fsd1/1Sj1V16AZ0pUTSGTzhKLpqisKaSva4T5iyrQNCXvOUgkuq6RyRj53q2ZrMFoPElZqY/ysoI8W3OsyNk5IbVZXWVxTHw+J26Pg3n1xRaLULHqKdwuO1JKAgE3CFis5HQ+BayeX0l1sZ/WvmEGQzGS6Qwuu41VjZV4ptGOvRQIIdh+yzJ6OofxeB2sXD9vzjOJaZg8+vM9ZLMmf/jFO1m6unbOBuTb/+tJnnpwP+6HDoKErTssQxJwOgk6nRzs7qU+GKA+GMAwTXa3dfD+NVagLuh00jo6yvKyyQ2tw5lhHuz6F9rjZ9lV/j5i2QixbCT//mhmAKub4CA9ibbxMSHZO/wsh0ZfZVf5+1kT2D6zOhfTS1mGMsPYVSeOaQog5wJVCArsDhLZDG5dZ3VZBX2xGOV+L199z07++AePMWZ+FEWwc2UTiXSGgy1dJNJZ/u35vXQNh/mTO68l6HHmU9Nuu84nb9nC53/8JABF9iCFdtg3ctRSnfMtoDPeiynNXHP1TpYXXFpMTBEKVa5KTGli5iQawOoDM9bP+sLj6YrGPdXbJmnPWo23TJq8VXxjxUeomaZhuSoUFhXUXNrFnQVXbEQURVBdX8z5k930do1QVOIjHk/j9o5HxXWbit2ho9s0bHE1T94YjsT59Dd/zZ1bl3LvdSvzs+kYJs0+Yvy14609vHDwHKvnT+2re6ZzgGQqy+/etRlVsYhRWcMkFE1QXewn6HFR4HHidzto7h0i6HPhntB2sKN1kOGByJTjzoaVGywew7GDbbNsOY7RoSgvPnGUTdsX4nTZOLy3ZdZ9pJS88PgRnn34IKYpiYQTfPOrDwOWIUFY5QLRdJpYOk3aMHBqGu9dtRyv3U7WNHDZdJyalueSjCFmhBlM9bDItwaP5mMg1TXp/Uh2FBOTcGZkynvVrvlUuRqwKy4MmUER0wfnxkKDF55Tf7KTQlsZ+iwtGyftN/G4QqAIKLA7EEBXJEyx24rl1JcE+aM7ruGx/ScBsGkaO1c2cd3SBk509vPL14/w4vFmnjhwioFwlD+/50Zqivx5Q2LXtbyEgiIUDGlSoHuxq1Zg2KO5eKH/DbIyS3AabdS54kSoi31D53lP/VY0ofKdc8/j1Rz81rxtqBdcM01RKXNO5nJkpck/nXmau2vWU+oI8s9nn+H99dfgm9Cs7a1ItV8VTyQaTiAUgcttZ/cLJ9l83SIcThtSShwuG1W1RQwPWg9mZW1RXrDYMEz6R6N4HPZLcqwMwyQcT7FpSd2UWS2dNTjZ1j+J3t09FMbjshNPZYinMhhS8uvXjuGy61QEfXkjApCMpfnWVx6ht3sEr89JQcDF5dHcYHgwQixi6dLZnTolZQWTjiWRFARcnDnRxdkT3TMchYm6Ttb5myaVtYWTNvn1j1+ntNLP/CWVJDIZRhNJhuMJ2kZGceoaxR73FKbphVmbckcdH6r/M1yqB4fqJmUmiGXDBG3WEmsw1cMzfT+j2jWfZQWbxod3KTfmNJtmZJruRAuLC9ZfoN8xCy6IeHeGw1T5fNhUzfr+5fjYgh4nygRu0JhxWFVfwbKaMs70DPDjlw/x3NFz/OH3H+Uv7tvBkurpl5YKgutLNud1VHaUbp3EAL7sgHg2zUAqgiZUkkaawyOtfHz+jhn7WFvxqzAjqRiqUDCkwZlwD52xYYa1KCdDXbTFBrArOnuHz3Nr5WoCcyw7uRRclcCqYZhouorb48Bm1xkdiXHozWaWr6nHyJo4XTayGQOBQNNVkok04CaVMchkTaLJCwUhLw5r1hFo6lT9hgsDjQALqoqRQNdgiKbKIlRFoabEj9dpR1Mn31jzl1Tw2a+/i+bTPSxbU0+g6NLKoscgTclffu4X7H3FyjSs2dTIp79811tKutJ1a7ZcWFLM8rIyvvna6ximZCSR5N/2HiDgdGLXVByaxmgiyc4F89laN168JoTIU86llOwZepo9w09zS9n7WFKwfsrnHQ29jk8PUuOaf0ntUS+8AgPJLiLZERb51ky7PXK6/BhcaEV8dkd+nW9XtWmXTdNBUxUWV5Xy5ftu5B0blvCdZ9/kcz96gi+86wbWN05VEBNCTGGnXu63mjazjKZjFNl9KELgVHUE0BoboNhRwPLA+PcjpaQvGaLY7su3AlGFJRHq1R0Y0sSp2SiwuXCoOg5Fp0B3gRDsGTzHjvLllznKi+OKjYhhmBiGSUHATSySwuN1sHxNHZ1tQ2QyWVJJqyO702VD1VTsdo2OlkH8QTeReBK/x0EkfmlGxDBNeofDvHykecp7J1r7JqVtx5ZUrx1rYSgcx65ruOw6BW4HZUEfpYHJRkIIQdOSSpqWTF0qXQqSifSkZVHTkkpc7kvT0rwcmFKiK5bA0z3Ll+bShkwKoo1prnjs0y85pJR0JZp5ZfAR4tkopyIHaPKumrJdg2cpz/T+lBPhvWwtug2vdulFg6Y02DfyPEsLNlBir7xgCWtVyZ6KHMCmOEibSSLZUZYUbLDGeUmfNDs0VWX1vEr+9wdv5/GDp/jW47v53Z0bWVp7aeSrS0E4k+Avjz3Eny9/Z+7crfN/rf80RXYPB4dbAUiZGdpjQxwZbef+2k2sDNRZ5RqqjX9ofY1rSxejIAhnErTHBtEUlWg2SXO0HyEsWUqXapuWM3SluGIjousq1XVFAAQKPSxfW4emq9TPtxrsFOaaOAWLxmnGwSIvqqYwFI5TFvSRyZpkslYGZjaYUuJx2tm5bgFbltZx4RxQVxYkmcpMuVgOXUdVBEUFbhw2jUg8xS9eOsx1KxtZUjd7RuRSEQ0nGOq3MgKqqtC4qOI/hfotgFWV5QhgXuHc6x/GIKUkkh3h4e7vkjUz7Cx7D5uKbkafhsbuUr3cXP5bPNbzA77X8lVuKrufJu+qOVfGSik5Hz1OKDPE3VUfn7KUcapu1gav58DIS7TGTkFOAyRoG0u7TzYj6gQvNK9jc4kQQuC069y9YSlrG6oYisRn3+kKoCKwqRoubXxJH84kaI72c0PZUlYEagE4MNxCR2yQv1h+T76zoJSSs5Fe+pIhShw+UkYGTSiUOwNITOyqRpHdi6ao3Fa1hse6DrK2cB4Nnqt7v19VPRGX2z7lPU2f+YbqGQpTWVyA064xFI5TXjg7xft4Sy/HWnpZWFPCsZbeabdp7xvlxUPn+fCu9WiqQjKdRVUFpoS2Piutd+3yeThtOl7XWyMm1Nc9SjQXD/H4nFTWFc6yx8Uxd7q4RJAFNKS0/h/zxixYrRJBIi542CXSIoN1fZdIZoR7qz/JQt+ai/ZBsStObi3/AA93f5eftv8dGwt3cl3J3diVqVIK459joT/VybHQG9xW8SHc6tR6E4HC9SXvYkvRrnxLDQUVEyM/3onXx5frjmhKiVu3TamPMk1JxjBQzLk9QBUBHxUBH5GkJQKUMQwy2XFuxoyLLDlB/3dCp8WxzIvXOSGNLcSEmIf1//7hZm6tXEU0m0TPtdXojA9T6y7GrmiTljdlDj8fathOge7iVz172FayiFJHAXEjhYKCT7f62FS7Cvl52+usCdbP6dwvBf9ljFUpJWe7BplfWURpwMuR5h7KgrOLD0XiKQ6e6+LGNfMtVfmswfnuIeaVB7HnPJmeoTCtvcMoQpAxTH71yhFSGQOP07qxGiuLeP1EGysaKqgpuXzdjoudW/OZXrIZ64arqAniD1xebGXseC1n+tjz8mnueu+mSWS+qduGyWZOomkNZLOn0fRVgBvT6ABUTKMLS0vTi6otmnRDJs04j3T/G5HsKO+r+yzljrpZr82YIbmt/INEsyFeHniYkXQ/d1d9HMd0vIecMYtmR+lOtLCj7L5pDcjYsQViSmFgPJd2nhgoNqTJ+ZERUtksQgiaR4ZZW15JpW98YnrzXAcf+tbPZ9ZVnAGGadIxOMrvfvvBXCxCjv3Lj2Oi0YAceSz3t2VArAJTv8vB3/32nRT7xgKck/kvsWySrGlyTcl8Hu8+SCSTwKs7ORvpYWf5iinXqdjhQyJ5rvcYjd4ydpQvR1dUIpkEBTYXxQ4fPYlRvt/8EmuC9TR4r77X/V9mROKpDM3dQ9y5ZQnFfg/ffWwP169qRJ+F+KWqCnZdo9DnRlEEI5E433rwVT7zrmtYv8gKQnldDpx2HUWxrPz9162keyjMkeYeltWX0z0UZmVjJQOhKGVBb974XC1ICaePdeb/XrC0Ct12eYS2MQPyl5/7BZ2tAwwPRvjwH9w0rSGxWKxDZDNHEcKGNGNkUs9jc+xCCDdSJkDYMLLnwNRRtSYsj0USM8I83v3vaIqN36r9YzyXEN8QwnrQb6/4MN9r+SpHQ2/Q6FnGuuCNU44xVnHtUr0s8q2zWi5c5k090RMZSiQ4NTRAmduD3+FkZVk59pym7NjxQ/HkJObqpeJk58yNqOaK7uEwb5xp47Y1i6Y9b5uicW3pIjRFpdwZ4NhoBysCtQymIszzTuZ9ZKXBoZE2opkkXfFhKl1B9g015722LcUL2DfczKOd+3lH9QZWB+uuumYr/BcZESklZzsHUISgsbIIh03HlJKjzT2sml950ZvqwncSqQyqorCgZnp6etYwefbAWTJZg97hCFnDZDAUY2A0armDAS+VRdPTv4cGIrlM0qUhncpy5pjFoxACSir8dLUPXfJxAEYGo3zrq4/Qft66gR/7xZsI4MN/cBOOKYZEYmSbUdVKYIz7ksU0h5HmCKbRjWmOIGUKTa0FMkipMpoZ4Lm+X1LvWcwq/zbUXHXsVB1POaMPL4SgyFbOjtL7+FXnP9GbbJ92uzHJiJnenwtSZoKsmcnTxAEK7HYKbHZGkklaQ6OkDYOVpeUUOsc5Eg2lQbYsnN27uhDpbJbnjp7nhmUN2DR1UqxFjrUvmfj/2PLFHPdADNMka1g/kURqQkpYkpEGp0PdHBxpRRNqvoy/yVvOv51/ASEE1a5CK9MyAZpQWR2sxzBNHus6wAJfBcV2Lw927MWrO7ixbBnRbJJftr3BooKKPK/lauO/xIgYpuSR3Se4ffOSPEfj5vUL+fen99FUXYzbMbNU/oXVpz1DEQJe55R9xjZTVYVVjZU8te80taUB1i2sZt+ZTmqK/ZQFvbjsM1/Ycye6+ce/fJRoOInNruGYI7s1mzEYymVmhBA88O+v8fCP35jTvhfCMExSyQw+//gN9Mozx/H5Xdz/kWsviDkJdNsaspkTjD3tNvs2hFKASQYhfVa8xMximkNImSFuptk/8iJbi26l9AKt0nB2mIMjL6MJHZ8eJJQZIivTMwZOhRAs928mZSYotU/fXMl68K06kYv1a7kYTGnm/h+PT4wmU4RSKeYFgvgdDlLZLNW+yTG2xvIifu/mTZdsRCLJFOd7h/jojRvwOK6eTCfAYCpCc6SPM5Eemnzl+WwMgE93UukK8u2zz/LHi2+fMoEKIVARGLnrqAkFRSicCXdzbeliq1tkTv9VvUgpwpXiP92ISCnZd7qDTNbg5g0L8l/o6qYqHnvjJN99bA+/e+dmdG16cZ+sYWLXx4v39p5uZ01TFYoyPnNqqoInFzBVhKDE7+H2TYvpH4kSiiVZ3VhJMp3B77m4fMH6a5rIZG6m+XQva7fMp7isYA7rackrTx/nO39r0aTr5pfyZ399H/pFjNXlQFXEFFlGy3sAKVMIkUa3rSOd3o3Nthkj257bxolkzCsSOFU324vvynsfE+HV/CzyreWNoad4uu+npM0kNsUxoehtmnEJjQ3Bm/LjuRCmNBEIqp3zqXE3Xda5x7MRNEWf1L844HBQ7w9Q5vYwmkpQ5HITz2QosF9YxzK7ql5+y3wNz3jq9VIMkJSSdLYZXa3EMIexpAidxNOH8di3AVBs9/HFZe9keaCGwyOT2c4SiSZUUkaGeHai98KU7cb4IpFMgt5kiCpXIXHDKjcptHuuqrzohfhPNSJSStr6Rnh2/1k+cddmnLbxB0tXFX7nto38yT8/gqIofOiWddN6JIvrSmmqLkYI6OgfZf+ZTr74vh08u/8s/SNRFlQXU18WZNm88bJvRREEPE4CHieRWIq2rmEWzCsha5iTyGYXQlEUtt64hK03WqK1c7mBDMPk1NHOvNu/cv08KmoK/1PSuwBCeND0RShKMaBhs21BCDu6bR0gMM2h3HsghM0iic3gWShCpcRexW0VH6LUUcOL/Q+ytehWyp21s4xh5nN1KE5uKX8fZbMcYwzTPdSKUFni20CTd2X+fbumsazEMm7FboudWzADD2amz0mkM7x0ooWTHX0U+tzcsKyRyuCliUJdCMMMkTF6cOgLCMUfx6bXAWDKCKrw4dOdrAhYNVOWl22dT9Y0eL7vOJFMgr9YcS/fOv0kLdF+dlasyC9rxq6Hpqh8cuEtuFQ7P2h+iTXBedhVjce7DtIRH2Kpv4asabwlHBH4TzQiY9mYZ/ed4bd3rac0MDkTI4SgqriAz95/HX/+vac42tzDPdtXsKapioB3PF0Y8DiREs53D/HtR17n/utXUV3ip6KogDMdA/z0+YOc6Rzg2hUNvO+mNXic9nyUfGAkSiicoKt/FKdDJ+h3EfC5Lh6DucSLPjwQ4XiufkbTVdZvu7T2ClcKIWyo6ngzsAu1MlW1BNQSumIhukf7WFNUPaMCvGGaxLNpPLqdtYHrWeRbg08LAMJqgZBKciY8wKrCyikd7WZCkb2CIvvsnBkpJdKUCEVwcG8LFVUByioCAFY2qOKDCAThUIKRwSi1DcXEY2mEsCaNSDhJoNCNps0+A0sp6QtF+caDL/Li8eZ8f99fvn6Uv/nArVQELs+QSDLoagVCaEiZwWFbRDrbhtO2HFMmUOTkZ8DMhYrDmQQv95/EqztzdTQKf7z4dv7q+MM82nWADzZsZ3vp4ryOatY0iGYSPNq5n1Knn10VK7EpGu+sWU9rbJBvn32Wl/pO8LVV7562H/aV4i03IlJKUpkse091MBJJ8L6b1uJxTh/zEEKwan4l//NDO/nrn73I4fPdNFYWEfA6rQxCMk1r7zD7T3cyEk3wgZvXsajWSllpqmBRbQlfeN+NPHfgLN968DUaKgq5aZ31EIejCQzDzLE3we200TsQxu91XWrG76LnenR/KyNDVgagoqaQxsXlb5kXcrkNjAD6EhGe7jzNmqKZG0NLJD9vOcSaompWBCvIGg4OhbrpiIU4ONSJKhRsikqNx0+Fa26FZ2PjNE2Zu+7TV/WapuSBH7/OzjtW0dE6SGGx1yqmzBhourXOTyYzICXHj7QjFMH5M734Ay5Kyvw8+9hh7rh33SSS47TnKCU9IxE+/+MnOdA8XlSoKgqJdIbu4fBlGxFTxomn96MID7pWgWGOAhIpM6Qy59Dsk5MBLtVOrbuI0+FuNhXNp9A+bmSqXYV8deX9nIn0sCJQi4IgIw2Oj3Ywmo7j1uzcW7uJgG1czEogqHcX84Wld/NUz+G3IC9j4T/FExkOx6ktDbBlaf2USt0LMWZI/ukz78STq22RUtI1GKK5ewiP086ujYsI+lxTGnILIbDpGjevX0htaSAvOQBQ4HXS0jlEwOfCZrO0RCpKCq6aAQEwsiYvP3UUaVoP96btC3F7Lk+8ZzZIKYnHUrz27Am23rgEp/vifVtTRpa+RISg3YUA0qaBTdVIZK3sUySTImVkqfGMCyKpQmFNUTUv9pxjRbACVVGocBXgtzvZO9DO51bdiD7HBlgX4mxLH7v3NbPjmkVUlPot4z5h/JFQgvBogoHeMMMDEXo7R0glMpw73cMNtyxH11UGekPsfvEUG69dQCqVIZ3KkIinSaUyCCGmBMItD2NCXkRKBiNxvvjTpzjQ3IWmKNSVBNi2qJ4NTTU0lhVS5HUTS02foZNSMhpLUOByTntfa4ofTS1FEQ7s2jyS6WOoShCbVo0iFk/ZflFBJYsKKq0IzIXEOyEosLlYVzgucq6jsjxnUGb67oUQuDU7d1dPrX26WnjLjYgQgoppUqiz7TMx6CmEoLKogMqiubVlEEKwuG6ysLBhmPQMhFEUJR94nQvN/lLQ3THEsYNWANPltrNtx9wbAF0KxgzIv/zV4zz/2GGOH2zjY5/dhdM1syEJpZN89dCzfGLRZlSh0BMPM5KK0xIZBuD1/laimTSfXnpNfh8hBMsC5TQVWDGUAt0BunUsTSh5ScF4NoNLu7RmXoZh8sNfvMGvnzjE9s1N3LZjOfNqinKVtpKerhFuvXsNxWUFHNhznvr5pRQWe2loKkNiLXMqawpRVQVfgYt4NEU2Y2Cz58YxzVAi8RSmJE+uj6XSfP2BFzjY0s3ahires20lG+bX4LlIdnDSOZiSbz/zJjXFft65cRm6emHTLgPDHAHFTzJzCk0twaEvZCT2Kwpct6Grkz0R5YK0+mwYy87MZbu3Em8bjdXZcCUXQkpJz0CIlYuqGA3HGRiO8tr+ZtwuOxtX1CFNSSqZmf1AF/8UXnnmONFwAoCmpZUUlviIhpNXeNypSKczfP+bz/LcI4cwTckzDx8EuKghUYVAEwpLAmWoQsFE0hYdYUnAMrZt0RGSxrhgcCKbIZFNE8tmCKUT+O1O2qIj2BWNWDZNfzLK/sFOTCRv9rdzf8MqSpxzZ+XabFaV7fBojAceP8ihYx187XPvoKLMTyqZIZs1SCYztLcM0ts9Slf7EIl4ilgkRVfnMNffvMwqhe8LcXBPMwuXViIlLF5RTfPpXmKR5JTsVe9ohHgqjc/lIJM1+Oen3uB87xBfvncHNy5vnKRqdiFMaU55wFVF8MHr1/LFnzxF28AIn9i5aRKlPZE+jBB2pEyRyJzDptWRzrajqxWkM81oShBxQf/eaCRJPJGmtHTyxHslS9e3Gm9bIzKxVuRidSNzqSmRQHGhF5umUl7sY+G8UgzTqmNQVYXujmH+7n8+RG/nCEJg6b9e4vckJXnNFICWM7388Yf+9dIOMkcYhsnIYBRVGxcvfPHJo+h2jY98Zue0fBZVKCSyGQ4OdaEKhXPhQXriYQ4OWXGAlsgwZa7xNXgkk6InHubkaB8dsRE+vfRaShxWn9dQOkmRw82KwgoUIVhTVHXJPARVUXJeh4HHbeczv3MjFWXW8tJm11m4tBKBIBJJ4C1wkklnKS4toLJao2lJBYoiSMQz3LhrBR6vg5bz/VTWFhIejaPbNdwe+5SAsQSSmSw+oHVgBAn888feQZl/9nKLeCpDOjtZ01QIQYnPzRfedQN/+sPH+fyPnuTz77ye8lzSwGlbzpjfY9eHyBq9mDKBqhSgazWQ+/bMnEi5ogh6e0OEQ3FKSibHYVpbBhgciLB2/dVr4nW18LY1IhnDZN+5DpbXVaApCm+cbqOm2D+ZpyHhVFc/TRVFNJTNnEYNx5P83SOv8oldm/PtASYu5Suqg3zwkzfyi397hSWra1m0vPqSBZfffPk0P/nOiwA0LCzn45/ddcn9Z64UqnqxLBN4dDtLAmVoQkECHbFRluY8kY6YxfIcQ4nTQ4nTQySTzOuWZk0TQ5qTysWimRRufeoDOxusGIj1u8/joKG+eDyFqwgUxaKsHzvYzvLVdRSV+jiw5zybty/Miwvpukog6MaUkvLKAK88d4KKqiCmYXLzXasnEfH8bidfuvfGfM1KQ2mQP7x9W16XYzbEkmn6QlGKfJNFfYQQVBcW8Bf33cSf/PujfOZ7j/Cle29kUVXJJC9DV4vR1eILDwtAa0s/jz18EI/XwboNDdjsGj/90esUFnkwTUljYymPPHyAnbesmNvF/U/G29aI6KpCJJHifz30Ep+8dQsPvXmCz9yxDf2ClN3Jjj4KvS4aymaukhVA++Ao9gmGYaIHI4Rg0fJqvvC/3o2iXpyMNB1i0ST/8tePI6WV1r3vw9tm1UtNGglCmRAl9lJrjY+gN9lD0BZEEWr+NX2GZkiXDosyJSXI/C+Ti8jGmKAzoTM2yi9aDrOltI75viIODnXxs+ZDfHjBepb4yy7bzdZ1NS8/OAbTlJw/3UMymWHTtQtQFIWzJ7p54tcH2X7TElxuS/hbURWkYeaJdooicuJYmhXgzh22zO+d5HEolxgQVlWFRGr6Ja8QgvnlhfzP+3fy2R8+xme+/whfeOcNbFlYN2siAaC42MfmbQswDZNAwE04nMDjdeDPsZTjiTTlFX4WLnrrMn1XgreOxnaFEEKwbXE9NUV+WvqGMaSlgBZJTP7JmiY+5yxiP7nAqpHLmkgpefNsB6e7BiYZE1W79GIwKSUvPnE0X3C3ZlMjG66dWag3baYYSg3Sl+ylI97KYHqArkQHKTNJR6KVE+GjnI6coDl6lv7U9FIHlwdJ3EhzcrSPE6N9tESGGUhGOZH7ezgVx2dzEM2kJq2/8wIC0upuqArBppI63tu4hnXFNQTsTkqdsy8HpoxGjpcmOHLFkmNIJTOcOtaJlHDdzqVomtUn6NodSzBNkwd/sodkMkNb8wDhUIJYNEU6laGo2Es0ksQwTPa/fp7jhzumTBaXA01V+INbt7BqXsWM2wghWF5bxpfv3UEqY/C5Hz/Jg28eI2NMbesw+TpYy5hAwE2w0IPDoaNpCg2NJazb0MC6DQ04nTbWrJ13yYbvPwtvS09kOBqnrX+EpbVl3L9tJa+fasOmqfjdTvQLGKY3rWzC73FiSokiBIZpWo2vAENKjrb1UFccIJZK0x+K4Hc72Heuk5+8coj7t165e9jfE+KXP3gV05D4/C7e+/HrsNlnvqy6sGFTbChCwaW6CWVGaHA3YWJgVxwkjQSaopGVWXRx5V6IVaYusasaH1+0hdWFlmKbKgSd8RArg9aDscRfiiElewc62FZWP+kY8Wya57rO5Ahl4/TvvLG5DPGfsVJ5AKfDNikIqqgK8xeWW3yQCQ++pqvcevdaS45TU/JiWGNbNC0af8jr5hVfcsn/heMbS7U6dI1NTbMr8Qsh2NhUwxfedT1f+tkz/OWDL9IxGOIjN67DbZ8+4B2Pp3n04YNkMgZIictjZ15DCbqu8eYb53E4dex2nbr66ZdCbwe8LU2bz2nn5eMt/HL3UbqHw/jdTrJZk/O9Q7QNjHKud4g3zrTTNjBKOmvwk5cP0dY/YvFJhkK8dqqVgy3dPH/kHF/7xfN0DYfxuRw09w2z91wnp7r6+dJ9N7KhqeaK3MNs1uAX33uFno5hhCK4672b5qRg5lSdGDKLTbWj5tbN4UyYQlsRWZnNic1UWAzGS5TnklIynOokbSbImCniRogToRdRhcnqwsr8jCwQZE0jL5aj5lTM9wy0kTbHZ89YJs0THacodXmpdBcwnIpzOtTPqdE+Tof6CaeTk0ry5wrTkPliyrFG7WPQdRXdNn07SkUR+abpqqqgqgrKTD/T6O3OaWym5OG9J9jf3JWnil+KLML1Sxv54zuuQRGC77+wjz/94eM09w1P+10qQtDQWEpJqY/i0gLKyvxomoo0JdmswaEDrTz28AE6O6bf/+2At6UnoioK79i4lOMdvZztHmRZbRkOm8bi6hJcdhsDoRgvHWvmhuWNgKC+JEjQa60fq4v8VBf5AXjlRAtrGqqoKwlg01TC8RRuu413b1s1SUrvciClZP/uczz7iJVeXbamjjvu3zCnyU8RKikzxfnoGTYEt5CVWXx6AVmZJW2mSBgaw+lBKpxV+c8KjcRIp7IUl83OlcnIFC3Rg3i0AKFMH9HsMGkzwWi2hyJ7DUIolDg9dMVDfOPI83mNiZSRYSSdIJHN5Gnsywsr+MKqHSwNlNEcGaLE4abUabFAJVDvLURXLl0rJWsY+YfC7bLNKXYw4/lmDIQiLloHBdZ1NExJ28AIsVSa+pLgFE6I1be3lb956CXKAz7u3rgU9TLGZpiS+eWFHGnr5ZWTrZzpHuQD163hrnVL8DjHqedOl41FSyrp6w0hpcTvdzE8HCWTMdi8tYnNW5sYHo7y+KOHqKwMXFSQ6r8Kb0sjIoSguqiAQq+Lh988ga6pGKbkH594neuXNRJJpBgIRznc2kM6Y/Dw3hP8+b03UuAeZ4eapmT3qTZuWN6Yn3lX1JVzoLnLail5hQZksD/M9/7uGZKJDMEiDx/9w524vbM3uU6ZKYbTgwRthVQU3UBL7BwDqX4qndVIabKsYBWmNEmaCeLZGG7Vw/BglL/98wcIj8b57Nfvoap25kxUNDuMRytEFToDyTaK7XUMp7tJGlFMjFzAFkocHr6+7lay5uRgqk3RJrFQy51eynMxj2q3n99esDFPLJNS8tGFG/Oan5cCy323fve4L6+eQ0pJIpnh2//xMiVFPu69Y+2MhkRKSTiR4h+ffJ1H958klcmyoKKYz951LctrxwOWnUMhvv7gC4QTKcKJAb7+wAuXNbYL0ReK8vPdR9i6sH6SEQErZd/S3E8mY7BgYTnShLNneunpHgWgojLAho2NtLUNsnDRzHGZ/yq8LY3IGI539FFc4CaaTONz2UlnDIajcTwOO0U+D/FUhqrCAvxuxxRdkJ6RMCPRBCvqK/LBrcrCAp48eJpDLT2smnf5wsmZdJZ//9ZztJ7rw2bTeM/HrqOiupB4bG6q9QUUkZMJpUHLtd1MgZvxWcZNAZjQPzzK3/9/D7N/9zkAvvE/fsGf/uXMhkQVGkdGn6Xc2UiRowaPFsSuuHCqXtKmTjQ7jFcrstb6qg6zOBETP8OmakwUaBNCoF9Cq4iJSGey+UWQz3vpjcHHDMg//eAlHn76MDZdxetxcOsNy6Z4NVJKosk0X/3lczx1+Ew+oHuso48fv3qIpdVl+fS4z+VgzbwqekYiBNxOHLYriEvlKoNH40kqAj6+9p6bJzUGH4NpmESjSUxDEoum0HSViooAmYzlrRmGybyGEo4d7XzLKnGvBG9LIxJPpTnR0cdPXznEH9y+ld6RCOUBH93DIVbWV/DYvlNoiuWtvHSsmYmNqsFqkv3w3hPsWrsQp00jk7CeWFUR3LZ2EX/70Mt8/p4bqAxOr+15MZim5IkH9vPC40cQiuC2+zdQVVvEn/3uD4hFUxb/Qbm4CJ1FLrJ+F7mq05mQSKQZ7A2j5G7y5jO9/M2f/Yo//fq7KK8OThm/U/Ux37uBuDGKQ/FweOQpCu3VtMePUu5sQhNX5g5LKRkYinD0VHc+gH05ONvSl9+/u3eUZ185dcnH2HuwhadeOoFpSpKpLP/4/Rcp8DrYtmH+pOuSMUz+8cnXefrwWQSCykIfa+ZVsn5+NZsW1E66/j6nnc/ffR2pTJYdK+azYf6VtZxMZbKc6OyjpMDDgoriae83f8DNne9YC0A8nqKtdZC1F7RjHWsRK+UVxYvfErwtjYiuqUSTaXpHIxR63bxyvIUlNaV0DI5yoqOPTQtqeGz/KU53DbC+qZpH3jyZ31dKyZG2XrKGyeYFUyPq88oK2bKojj//8dN87p3X0Vg+d60PKSUH3zjPf/zj82SzBltuWMx7P7Ydp9PG7fdv4LlHD7FmUyMNC8sRF0nHPfvwwTxVffm6et7zO9svyZhZxa9tFg9CKZzwmiBpRGiLHabRuwGXZlGnfXoxsewwPq0YiXnFM5nLaeeN/c0cPdlFVUWAkkIvDod+SYSzo6esoKWVUZM8+swR0pksS5rm7q57vQ7edevqSa91dI+QSGZwOcdboz5x4BS/2nOUhZXF3L91JdsW1RPwOGcsdLPrGiUFHqsbgPPKSue9TjvbFtXnjz0bHA4bjfOnF32qqy962xkQeLsaEVWlpsiPz+kgaxiMxBI0VRTzzKEzlPm9HGnrRVcV5pUWsu/8uCCylJKBcIw3Trfzvu2r8xXAY2lOsKLh79q8nO7hCJ/+7sN8bOdGdq1ZgKZe3C2XUtJ6to9vfe0RIuEEy9bU8YnP3ZZvk3HdrhVcu3PZFK7JmBL42A1ryQW05N/3B90sXzt7dfOFSCfOYGQzKGoS0+hEs61FoKEpdsKZARyqh8FUO169CAWLoZqVKUbSvRTbZ09XzgQhBG6XjT/6+A5S6Swel/2ygqLf+IenOHGmB7td40P3baaze4TnXj3J737g2isKsl6I7uEwv3j9KL9/82buXL9kdk5RDpKpUpxXgng8jSsXFI1Ekrjd9mmzR2Ns3QtxJTyXtxpvSyMCMBqzUodH23rZtrgeu66SNUwWVZcS9Lr44YsH0DUFh64RT6cRWMpQ53qGuG/rCgpcDqLJNC8fb+ZkZz+pTDZPcbZpKp+6bQubFtSwpKZsVuqzlJL+7lH+95d/TU/HME1LK/nD//kOgkVWLUkqk6V9cBRVUfA67RPaAVhyjk8eOs2O5fMve309uY4oAzKFxEARLoTQUfUFjAU3VKFhU5wMp7oYTncy37OBlBnjXHQv4cwAqtAotluqYpmMkbtpL+0GFULgsOs4JsShDMMEyZwYv5msQVfPKGDFQ/w+J6oiaOsaJhJL4vdN02pimmuSMjPsHjhFlbsIh6Ljt7lxqjb0CQ+h12nnq+/ZmW/QPWdIaQkqX4UYRCSS5MEH9nHvvRvQbSpPPHaIlavqCIXirLtg2fLfEW9LngjAkppSPrlrCx6HjeW1ZQgExQUeq++o0862xfVUF/nRVIXtSxpQsLyMjQtq8OeyNB6Hje1L52FTVW5ftxibNqbNank7Wxfn3NqLFe9JyfBghL/98q85c7yLpiWVfPar76Ksclx3I5XJ0jUcJp5KE4olJu17uK2HgNvJsY6+KQVcF8I0QxjZ8xjZlkk/2cxBjMxhK8iWbSWdfA5Vm4dQPGTSezGyrRPqWRTWFd6J31bKQt82dNWBRytkbfAOlvi2s7Tg+vzn7Xv9HN/5+2dIp7LTDwjyntxMf4+hrXmAb/3NE0TmULUcj6fp7Q8BUFLkxeWyURi0vtvT5/pm5UNIKYllk/yyYzc2RWMoFWbv8Fme7ztCOJOYtL/P5aC2OHDJD6opJW0Do5e0z0xj7WgforS0gJMnuxjojyCxlmLAFcWV3i5423oidl1jcY21NlSEwGXX+fjOjaiKoMDlYOuiOgB2LZ+PkTE48OwRVmxfQiadxelx0HqsA6fXQSqe5v7Viyks92NkTUIDYVKJFH2tA9Qvt8hmvsLpadtSSkaGovzvL/2aI3ubWbyyhj/6/+6mYpqApqYqaOo4w9IwTZr7htFUhUQ6S5nfy4N7juXXx9NBCC9CdXNhCbGiVk94zQShoqiVuX0cCOHIR9usjImDST6PAF1MXdunU1nisbQl0nQRtLcMUljsQdc1wqEEqVSGyguugWGYDPVHcDhn97b6BsOMhK32lPNqitA1FU1Vaawr4dW951i3qu6igWmJpD8VYp7bqtcZTUfx6S6WFdRyOtzJxqKFV6ziJRAMRWJXeBTLSLS3D1Fc4sPMEcgMwyQaS1JZFSSZzOC6iA7Mfwe8bT0RsIyHMvHhyCnAj/3EwnEOv3CcjtPdCCHoax3g1J6zmIZJ85FWpCk58tJxus9003Gyi0Q0wdkD53G47CRiScysOWO17ljfmf/1xQc48Po51m5p4nPfuHdaA5LKZCcV9xmmScYwKfa5WV5bjmGaVBf52bV6IcGLKswrCKEhhHrBj/WatY0NIbwgo0hzFKEUIITjstiMQhHoNnWK7saFePSBfTz7xFFOHOngqUcO8sNvv0T2Aq9KUQQ2h4amKhjZqdobY5BScq6ln3Ta8n4WNFqGQFEEK5ZU8eaBFkZDF+9/qwiFencpLs1Okd3LikA9CgrP9R3Gq196ung6rG2s4mM7NlzxcVLJDCtW1HD2TC8NjaUYhondpjE0GKWvN8TghKbv/13xtjYis8HpdlCzuIroSJTwUITi6kJWXr+U0ECYRDSF3WkjEUuRTqSpXlABEmwOG3aXHSNrkoglUadpTSGlpKdjmL/8H7/g8N4Wdr5jDX/ytXdSVDo1JSylpHM4TE1RgLGebG0Doxb70O3Mdb214HXar7DbXhojex5NX0w2cwrT7EVRCslmjiPlyJyOYBimFb9gPFU4xkWY6cEXQtC4oIxV6+dR31hKZU2Qnq5ROtoG8z/9vSES8TRHD7Xzrb9+guHB6IzHszIzVuHdwsZxBbolCyqIJdLsOdgyJ6PYFuvHrTk4He5imb8GCSz0VU9RCMsH16dZis20PNu+ZF6+7H90KEoqmbksQ2136KRSGaLRJH29IZxOG8tW1BCJJDh1qptA0P3f2guB/+ZGxDRMzKyJw+0gFo4z1DNK8+E2fIVeSqoL6Wnpp35ZDcl4iiMvnSCVSJGMpTh/uJWS6kI6T3fn9VDHIKXk7IluvvJHP6X1TC8f+tQOPv7ZXfgKpleFH44mKHDZKSlwU+hxcaZ7gMOt3ZPiH3ZduyqpOWlGUZQShBJE1epRtQUIUZDrtzs3VfMj+1v5128+m+vsJ/Kvv/naWYYGItPXd0zpbwOJeAokkzxDsDgPd9yzbsYixHgizYnTPQCUl/iomKDgVVbio766iMefPUoqPXOcJj8uoeBUbXTGBynQraCqgsUTOrD7HAO9IUaGojSf7uXA7nOERuIc3tvCyGCEkaEoI0NRzhzv4sjeyUZr4jkZhsm//PXjPPfooUnXMRFPY5qzN99SVYXqmkJ8Pid+v4vCIg/BgJuXXjzF+vUNpNPZt21NzFzxto2JzAWaTaO4Kkh0NEZxVRGtx9pZuL4RVVeRUlLVVE7LsQ4CJQUUVgQZHQhRWBEgFopTNb+c3pb+ScVjhmGy56XT/NNfPYbbbefzf30fKzdcvAS7wGUnmAvOFvnc3LxqwZT1+NaFdVOqjy8HilqIolq8EFWrA8hzLeYCaUpeePoYdodt0jkpisKCJZX87Aevcstdq6mtn0yKEkKQzRikUxkyGQPdplHfWEosksSfm0kz6Swul43qusKLXq/2rmG6+kYBWLG0GucEFTZdU9m0dh7f/clrHDrewYZV9RedpZcUVHNktJVqVzHHQu2oQiGcTeBTnDz6sz1s37WC4vICDr5+jsG+EKUVfn7yLy/wvt+7ASX3fex95QwDPaMsXVM3RdRJSknL6V5az/ax/poF7HvtLABGxuCxX+zl1nvXXVT2YQyGIenuGmFwKEokkqS/P8xtt69i375mqqqCbNl6eU283i74b21EshmD0YEwpbVFLFjbQGQkSjJuUc/rltTgDXioXVSJw+3A4baj2zUUVSGdzODxu1h5/VIcLqsvTSqZ4eGfvMGvfrib9VubeN8nrp9TsdtEfolVozMZQoh8VuhqQkpJNJzkhccPc92tlkTgbFmmjrYhhoei/PEX75zSYDwQdLN9x1K++Y3H+f0/uYW6hpL88aSUNJ/twzQk7S2D2GyWkf75f+xm7YYGVm+Ym2SflJI9B1pIpbKoqsLmNfMmXS8hBJvXzuOHv3yDXz16gFVLa7BPE/Q1TIOO+CChTJwGTzkVziASiV930RrrZ7m3Dk1XaVhQRlV9Mf1dI+i6hqarOF02Fi6vtiplpaT5dI8lpThNXCibNXjqwf2s3tRI05JKisv8ABx4/RzRcJyqurmV5+u6ygc+tA0hrCLBhsZShICCAhd9faE5HePtjP/WyxlNVympKSJQ6kcoAm/QQ3GVNVOX1BYhFIG/pACH2yIYefxuXF4n/mIfmq5RUORDURX6chyQ5x49zMc/u4tPffGOORmQ/yqMGZBvfe0R/vmvn+AfvvYo0Ujyom5xNmvw5MMHue/9WygITF2aCSFYsKSCJSuq+bu/fIxIODHpvabFFaxaX09DUykIS2h5y/aF/PP/eZqhwbkFB5PJDK/tPQ9ARWkBi5um1i9VlgdYtrCSA0fb2XuodYbyeYVadwkr/fVUOoMowuo1O89Txkp/PeRKD0wprSXvWImBMl4ekYynyWYN+rtGKSyePta175UzVNQU8s4PbOGlJ4+SSWeJRZM89vM9fOgPdlJ5kULISeNVBMGgh0DAjdfnzPNyFi2uYPt1i+Z07d7O+G/tiUz3IFwqBnpD/PWf/ZLGRRV85R/fPyV4KqXENORlaWbMhIlxGGlaQc05LK/ziEdT/MPXH7V63Eh46cmjAPze52+b1iORUvLGK2eY11TKkhUza6goisKuO1fz3BNHOXKgja25G3y8zmfyEqdpUQVbti/EZtOBxDRHnDyGk2d7aW4bAGDrhkZ83qk9eTRV4Zbrl/LmoVb+41d7WLmkCo978nYzjX/8dct4HN3XQnf7EOdPdVPgd+WruZHQeq6PJ361l9NHO7nxzlVTxtrVNsjZE93c8+FrcDh1lq6p44f/+ByxSIKtNy5h6Zq6K55k3q6T1KXiv7URuRrweJ184n/cRt380mmZm0bW5Jc/eJX9u8/lDclYwd/l3gJ9PaP5QOvR/a18/uPfv6RjRcNJWs/1WWMRAJKXnz6KEJYhcXsck5YiLef6MU3JdTctnZVSXlJewPablhIIjrd/kDKnx5pTJR+TYtV1lfd91KKpDw2EL3pc05Q8/vwx0hkDt8vOjdsWTfsQCSFYu6KWuupCTp7t4bFnj3HvHWsu+YHTdJXl6+ZRVVdEIp5moCc0HtwWsGBZFQ6njWQiTUHAnWemSikZHYqy79Wz3PneTXneS2m5n67WQUaHo9x4x2qkaSLFpctp/t+I33gj4vLYaVhYPuX1MTda1RRuums1p452MjIYYd6Ccsqrg3h9zlxc4e1zEymKIBZJTum6V1jkoXZe8aSOgDNBCMEHPrZ9Ug9bwzA5eayTWCzFuVM9eRV1S11sDnUoUtLaMcTufdZSZuPqeubVFM24vcdt586dK/g/336Onz60l3Wr6qivvrSm6Dmf44I4lSAaTvDcI4dwe+zoNo1YJMm//NVjfOYv7qZ6nhXj6Ose5cY7V+H2OEglMrzx0in2vnKa29+zkZKyAn71g9d48YkjXLNzKfMWlOP2OlAUBSH+7/EuLgW/8UYkkkjRPjjKgorivNqZlJK95zsJelw0lAYJFHr4H395jxUkzaUu/6tuFpmTMzRNiT6HgK0QgoLAeC2P5UlY9eTGNDR8IcQUBqsQsGRFDYuWWvKKQxMIUmPG9mJZStOU/OrxA0SiSZwOnbt3rbqoQRNCcN2WBfz6yUM0tw3y7R++zJf+6DacjrnLGBiGyblT3YRG43S2DebPyeNzUlVbBAIWLq/myN4WAoVeSnNlDFJKFiyrIh5Nse+1s5w60kFtQwnbblrKIz95g//xjfv45Bfv4OAb53nsZ2/S0zmMx+dk1cYGbn/3xresberbGb/RRkRKySsnW/jqA89z6+qF/M6NGyjMySw6dI0//uFjvO+a1dyxdtG0DaH+M8dpmJKRWIJjHb08degMQY+LT9+69ZIzP1JKDu5r5dDeFg7vb2XFmrpZ97nlztVU1lhM3ZVr6vLaJmNZmzMnu2k9P5BbW02Nx5w408Pzr54G4NpNTSxumr31QYHXybvvWs83vvUkbxxo4cEnDnH/nWvnrnguoKwqSFllgNZzfcTGanoEVNUX8cJjh3NGpJnNNy7OTw6ZtMHpox0MD0bRbSrbb1mO3aHz+vMncbntRMMJhCKoqClk1aZG7llcwbkT3azd2pSv6P5Nw2+0EUlnDX699ziRRIqu4XBeKlAIwZLqUlbXV/D1B19AFYI71y2+Yu/DzMUWZnJ7x2d1STKTZSSWoG1glOMdvRxq7eF09wBDkTiGlLhsOstqyrh5ZdMlV+CuXlePP+Di/Nlertu5dNbt503Qt7iQRFbfWILDaePAmy1cv3PpFFJdPJHmuz95jVg8RVHQww1bF3LqXB9N80rygsszfe61m5p49pWT7DnQwg9/+QYNdcWsXzl7QNM0JdmMQUmZH3/Qg9ttJxZO5mM7bo8DRVU4+MZ5Ws72cud7NuWPqekKC1dUA3DiYDvxaIpkPM3xg20sXlVDJEfJ7+4Y5vFfvMmX//632HXPuvyYfxPxG2tEpJQcaO7iQEs3K+sr+Iv7dlDoGU99KkJw25pFPLz3BKd7BmY8TtYwaR8cpbbYP6ukwFAkzi9eP8I71i/B73aSyRpEk2lC8SSDkRjdI2E6h0J0DoXoD0eJJtJoqkLA46Qi4OOdG5dR5vdQ5HXjcdgIemYvmb8QQlgR4XnzS/nSX907a/HdbMcSQlBRFeCzX74L7QItFdM0eeDxgxw81o6iCN7zjvWUFHn5yt89TrDAzV23rGTtilrsMyi7O+wav/Nb2zhzvo+RUJy//edn+Mqf3kVj/fQKYWNIxFOoqoLLM4NnIGDjtQv5yh/+mO27llMQHF/uKfn2nrBi/TxLT7cvTCKe4vpbV+DzW9smkxnqm8oonKYU4jcNv7FGJJnJ8v2X9uPQNT51yxbcdhvJzGSqdW2Rny0L69ixfP6U98AqtPv57iP88OUD/MGtW7l9zaKLGhLDNPnZ7iM8e/QciytLyJgmmmIpaXkcdvxuJ00VxWxeUEvQ4yLgduK227DrmpU54urNdkII7Par011PCKuNw0RIKXnzYCs/euBNTFOydX0jt964DKdD57Of2MmX/+YR/vyvHmb54krec/cGVi+tRlUnGyEhBI11Jbz/no1863sv0t0X4uvffIIv/dFt1FROLYQcQ2/HMFX1Rdgdk89vYp1MT+cw/kIPzad7GewLz1gXlc0aPPSj3WzdsRRvgSv/el/3KOVVM4/hNwm/kUZESsnzR8+x91wHTRXFvHaqld2n26ZsNxiJ0TYwymMHTvHqydYp72dNk47BURZXl/L6mXaW15RTXzKzdoUiBEJATZGfz919HTZNzb02nkH4v+GmlFLS3D7I//nOc8TiKeprivj9D1+H02GpxC9oKOXzn7qFL/3NI+w/0s7x0z3ctH0xH75vM8HA5II0RRHctmM5x05389wrpzjb0s9f/O2jfPHTt1I3TcZGmpKThzu49ubldLUN0d0+xNkT3fgL3UjTCkgf299K8+le/vir7+KlJ4/wPz/9I7bfspyb716LO8ddkVISj6V45CdvoNt1rtu1gkgoQVfbIE63nf2vneWam5b+X/F9XSl+44yIlJL+cIxvP/smGcPk5hVNvO/a1dNu+8Kx8zx+4BRfuucGltdOTQOb5lhjI+vv2W4oJWcsNEXBqWtv27aIVwIpJX0DYb7xrSfp6h2lpMjLZz9xExWl4wxgIQTLFlXy6Y/ewNf+/gniiTQPP3WYlvZBvvjpWykrmewV2G0aH3//tbS0D9LcNsjZ5n6+8I2H+JPfvYnlS6om1Q4N9ocpqwxQM6+EVDLN2eNd7H31DJ/8wh2YplXeYLNr3PHujWi6yq571lNUWmBJGeTiPalkhjPHOjm8t4X5iypYvbnRSmsLG/Foim999WE8XieLV9X+517ctyl+44yIYUq++9ybdI9Y5CghRN4buBCK1aAGRSiTliljgc9/e34vTeXF3LC8cU5FcGKGz7kcjGu3jhux/2pIKekfivDVv3uCk2d7KQp6+Nwnb2bJgqn0diEEW9c3cv9d6/j+z3ZjmpKjJ7v49n+8zJ/9wS40bXJNUmmRl09/9Aa++I2HCUUStHcN88W/eogPv3sLu25Yhi0XpHV57KzZMh9FEThddq69eRkrNzbgD7hJJtN8+svvoKxq3FtUVYWN2xfmPwdgZCiKx+fkng9tw2Yfj9fYbBqrNzdSVOqjIOjGW3B1tEv+u+M3yohIKXnxRDPPHzvPh69fx3eefZPm/mFePdU67fYnOvsxTZNDbd2EE8kJx4Hnj53jkf0nrQ5qiuCGpQ2zGoir9bBLKXn1VCunuwd53zWr8holUprAONcFMsBYoykDU0ZRxPSBQFOmMM1hVKWYrNGNplYiLqGnjJQyH7M4fLyTyjI/f/r7N7NySdVFafb33LaGA0faOXS8A4ADRzsYCcUpLvRO2lYIwYrF1Xz0t7bx9//6HOmMwWg4wd9/93kOHevgg/dtpq66cApPQ1EVAoUW+9bpsuN0TQ22Xji+ssrAjOcphKC2cXo19t9U/MYYEZnTzPyXZ97g92/eTFN5Ef/63Jtz4ptOrcyF65Y2sH1JAwA+lz3Xz3ZuVqJnNMITh85wuaLmI7Ek33thH+F4koxh8JHr16FrKunsWTKZc7idNwEKkcSj2LQG7PpyMkY7Q6G/oiTwl6iiYNLxpMwSin6frNFN0PdphsJ/Q7H/KyAhlT6K077xogZFSsnZln6+8a0nOdvSz9KFFfzxx29iXm3RRQ2rEFYLzffds5GTZ3tIpbPYdHWSFzIRiiLYdf1SOntG+PnD+3JygybPv3aawyc6uXPnCm69cTnFhZ7/F6v4T8RvhBEZa6H4zSdf4/4tK7l97SLO9AwCUF8SZOvC6bkHGcNAURRW1Fawom5yTMQwTU509NNYXojzElXcHbpGuX96Xde5oMzv469+axcAuqrkWxvYtHpGIv+IovhwObbhtK1nIPRlygL/h0TyNXzu+xmTUhyvrUkRiv2YeOo1yoL/gEBBoJNIvU4s+Rw2rR6HfQ3I6etEDMNk977z/N2/Ps/IaIzbd6zgt9+zhcAMIk4XQgjBysVVbFhdzyt7znLtpiYKLtIRT9dVPnTvZgaGIjz/yql8WeTQSIzv/XQ3Tzx/nBuvWcgdN62gdJrq3P+Hq4/fCCOSymT5/ov7uHHZfG5aMX9SfGOszeF0iCXTSCmJJFOMXKDi/uSh0/zDk69zx9rFfGrXFhz69FyHiRiLY/jdDlbWlb8FgVWdoPeTKIqbrDFA1ujC49hBLPkchgyhU8tw+G8JeD+Ogh/DHCCWfAZF+NDVSqSMkkgdIJNtAQyKfJ9DUQpIZ05i0xdPOZdYIs3PHtrHzx/eR9Dv5n988ha2b2qawheZddS6yid/+3rWLK/lhq0LZ132OZ06f/CRG4jGUuw5MN7DBwGJVIYjJ7pYNL+c0mLfJVy7/4fLxW+EERmNJ9m2qJ6VdRXjAdDcFHawtXvGIv+W/mEMU/LMkbMcbe/Nvy6lZDAS5/qljSQyGU529bOqbvbObaYEpERTFK5m4Z5lnNIgs5hmGE0tRwgNoS/Cri+hf/QLFPr+BFUJ4LCtQoixuIGC13knpkyRTO/N6Z6uQUs+gaZWYsoI2UwPkcSjFOpNkNOQN03J2eY+/uU/Xub0uT5uuX4J99+1ntKiy/OuxgKn77hlZf7v2bb3+5z86e/t5Gt//wT7DrcR8Lu47461XLdlIcVBz6QCwv+Htxa/EUaktMBDacHkdfJYWf/mplreP1OK9/h5nj96nrvXL5k2xTsGM1fboioXfwAM08SUEptmaa5a5KeZafDTQUpJ1jDRLiBmZbPtGOYQI5HvEPR9EkV4QChImUXK/7+9c42N6zzv/O99z23uHA5vokhKou6ULFuWfJHsVLHj2K6bdB1sm0XS3d6SdlEE2C7QxTZIi8Xuh2Kx6F5QLIIWLdovu0GCokAXaZrEjZMmsuM4tS1btqyrqQsl8U5xhnM7c27vux9mOBQtUqIqO6bs8/sgkZwzZ86Qc/7nPc/l/9SJ1Dxae9S9F7DMYVLOEUyj2UmrogqOfS+m0QNIOtK/TsN/DaUqCGHjtFYhWmvK1Qbf+t5b/N333mTX1j7++D/9Eru3b7jtAVjv5nafK4Sgu5DhD//9L/AXX3uBpx/by4F9q3ulxLx/fCREZMV7+euGaq+2TfOB5j+rPe4FIf/n6Os0goB/+8mHsVdwj1/EDyPCSJG5rhv16z8+zuaePAe2DqwpMHvswjjfOnaa//CLP0d3Nt1OG1vmdiy2YpnNVYTWdYSwiXQRMDFkJ0IYpBOfuG4l0qTqPo8UNg3/GJoIgYFj3UOl/k0S9v1kkp9GCMFC2eW/ffU5EgmLP/jdZ9i7c+OyWxetNYqoWVmL0coKKTQa0TLR06hm3GWF6lBYGjW6+PXNEELQ1Znm97/09A3VrjE/Oz4SIrISYRQBmmMXxjFWSZNcnCmSsq3W7czkittcnivxD2+eAyDpWPzGxx/AXMVjw/UD/DCi0OrR0Vpz6uo0X33uJ3z64MgycVmJxdTuO5Nz5JIJvvyZj7eFp7m/5nYCA9Pc3Kxn8d7ANLoIo0kc+x4EK3mxBhjGJhL2Q5RrX8e2tpGwH8b1XsUylwqq0mmH3/3i4/R0ZZkPRolkDT8KcMMF6tE89XCecjDFhuQIQ6nWlPuoyGjlKPfmPwMI5rzzVINZhjOPtPcrhKDkXyHSAaH2SBmduFEJW2aohrP0JnbhGGlWQgixajYn5mfDR1ZEgkihgR39XRzZs7LR8JE98OuPHbzlvv7NkebtkNEyplmNUs0ljBQb8kuuYZZpYJmS33jsIBs7bx0ILLseU6UKv3x4300L3LTWBOE7NILjFLK/hx+Ocm3hj8mlP49t7niXkCx97QWncKxF308NqKUOV0PS35dHa40R2ZxdeJ4uZysJowOlQwZT91MOJglUM1CtdEioPELVwI3KFP0xkkYeNyqxEEww712kL7GbtNXNnHeBrNVLNZhF6ZAp9zQ7co8z0zhDlzMMrCwiMR88H1kRySRsvvD4A3zinu0Mdd2eKbPWmroXUPN8enJrGz6ktWayWEFKwUBXs05DCNEOskohbmlduLjMt0yDfGq5BSIE1BsvorXCC88gSeCH5+nMfgkpOkjYDwCSUuVP6e38Hyz/0wsENkovEKkZDGMDoJAyC9zYxAaQt4dIGnl8VWO0fJSexHbKwRT1cJ5NmYcAqIdFKsEUbrTAeP04gaqTTHawNfMogW7gRiVMuVj8pakGc7hRiYSRRaMo+pdROkSKj+zH9K7gQ/fXCVSIIYz2FLRQR5jCeNcJByMDvWQSDr//te/wiwdH2NKzepXiSvzg7VGOXRjny88+xuGdawvoXZiZJ5d0GCwsFXutdit1u1Tq/48gGqe74yvUvReo+z8i5XyMMJpGijIgMGQnmdSz+OEotrkNIZoCkUl+Gq0DipU/J534JGF0hVrjeZQqonUDrYP2tgDlYJKSf4VZ7zz78s+iCIh0iFZVQu3jhiUsK0nG6gEEae88g6n9zHqjTNTfZkfuccYqR8lYvdgyQ6QDupxhbJnBjUqcKn2bPflPMeWeYiB1P9PuaYbSB5G3UUEb87PjrhaRQIW4kUekI7JmClOaHCueZltmkG47j68CXpk/xfbMINC83tajBoEK2ZnbhCEFV68t8OKZSwx2ddzi1ZZTrnuMzRa5NFvk0M5Nt0zYRkpxZnyGHf3ddKaXiqneq2BgOvkMAhMhkqQTT2Fbu3G9n1D3XkbrejNLQ4BAkEv/CiBRqkYYTeEFp5o1JcmncKz7AEHCvh8vOEOx8mdEao5s6tn2a+WsfgSShWCSSfdthlIP4Kka1WCGgdR+phunsWUaQ1rMNs4x711kzrtAqDxGOp5GCpNt2Z/j9MJzbE4/SKBcQNCIFhitHGVb9gj1cJ6UWWjuR1hoFBCLyHrkrhaReb/M2coYtbDBSG4LHVaahaDG1foMCelwtjJGt9OBQJA2k7iRhyNtZrx5oBnDMKSgP5/lyMjNp61dj9aat8YmcSyTR3ZtXlPz3bVKnfNT1/jiEw9hvgfT8K5HCIEhro+nCGxzC5axGBTV1/2/aGGom2lfQpLOIQxZgGVZExvH2kdf539H6aVivPbjAgxhkbcHmGucJ2v1ESgXT1VwZBbbSDNW/ScqwTSd9mY67SHcqMR04zTlYIqEkWNT+qFmJkhmaIgyV+uvszn9IJ3OZiIVMOW+zWjlR3Q5W+mwBzCM98b/JOa95a6uyBGAJQwcaSFb/t6mMCjYORzDak1Fy/J66SzVsM4/zb/djD20t26Nf7jDY7gVi8bPpmHw+N6ty92/3sc5rIvpXzfyaagANwqYahRpqOYcXsscQhjbuBYYaAQT7twy4x4hBFJm2vUk178fP6qhdNOoqRxMEmoPTUQ9nEcRITEYzhxmW/YIUhikzW66ne1krF78qMbm9MN0O83eo4Vggiu1Y1giSS28xnj9TU6W/p6uxDY8VUXpCFN8NP1L7wbu6pUIQKgVSjczLa/MnyRlJMlaaWy5dNWKdISvguuk42eLF0b83WunePbBPfTll3enRkqB1gRRtGwI+Gos+rSuFa01b5bOszXTz5xXpha6XKxNck/HMFkzxYQ7x7g7h9ZwrHiOSuiSMZMMJFduntNaM15/k9nGOQrOZua9MfqSI2StPrJWH35Uw5A2kfaRwiTSHs3SvgiNRukIRYTSYSuImqPDGqBqz+FGRaQwMYSJpikcjswylD6AKT84o+yYm3NXi4gGHGmhtKLTzrI/v4tz1cs3bCcQpMwVJsNxW+fjqsdw08e15uWzY1Rcj391+N5ltz5aa/wwwg1C/u8Lr5NJ3Ppqe+rKNFFrbMRaqEUNTGFwpnyZ6UaRndkhwihi3q+QMZO8WRrFlhbj7hy9iU5Gq+Mc6bn3pvvscobpS+7CFA5F/ypT7kmm3TNE2ifSAVKY7Ol4BilMzpa/T94epBLMEKgGSod0OVsoBVepBXMUnGEyVje2bMaJDGlhCIu02Y0hTLqcLRgiFpD1zF0tIgKBr0IMITGEpGBnidTS1bydicltISkd9uS24il/KULQWrrXPb9tUrRWKq6Hum7pvxoL9QZf//FxvvT04fY4ivbx0fR6TVomv3rkAP1rqBMp1lyuzi8QRmubu1kJ6sx6JXoSeQSCQIUsBDX6EgUqoYspDCSSTjvDnFfGEJLjpfMc6hrBFjfGIIQQJM2lIHTB2USnPdR6P0uyvFiheqDwOYQwMFZI0zbrP5r0JHZw/c1ht7MNgWR79rE1vc+YD467WkS6nBwP23uRohnlmPNLzHpFkkbzip6z0jiGTX+yeU9vSYujs8fYkd0ENEvfI6W4Ol/m+2+N3tZrX5wpopS+6S1IpBTfeOk4j+7ezCO7Nt+wElJK0/ADenJpUo59yxkyWmsGCjk+9+h95NNrG5JUsHMoND1Onnro4RgWCWmTNGzSRoKhVC+T7jwa8JSPRHJffhvWbdRmtG0PV7hVXGssQ4jl4TkRZ2LuGu5qETGEwfUV5t12nn85+Hj7w7wru9wD0zEsnux7uP19pBTduTTPPrCHzx7ed1uvHSlF0jbJJVc+mbXWvDJ6BSEEn390/4ou8ELAr3zsfrb1FejOrm38w28+9sBtZXekEAyn+1txI40hDGxpkjQcDNGMEtnSJFAhjrSo4/FG8R2O9Nz7gcSPYu4+xC2W4+9f6uCfgdaK2cY7dDqbMYVzWzUW9XAeN1qgYG9uX/Vcv8HF0mm2d41gybXtL9IhM+4ZkmwmbacohufJWf048l1dwlozUSzTlU3jrNKUt9ZGszuhGrqcXLjIvo5tHC+NUrCz7QzVcHoDZytXyFopin4FLwrIWimG0/3Y8tb+KDEfOVb8QNxlKV5BNZzjxemv4qv6TbdcTEMunqjlYJofTv0vGtFS7MM04VT9r5hwj6/5CLSOeHn2L3EZwzQkpxee41z5B63HFL6qt53DBgodK5oVRTrEDYvMeWeohbPMNc7iRZVmha3y8KIy5WCCajCDF1XwojK1YPaW8ZeVSBsJHiqMkDIdDnTuYGd2kJ3ZIbZnBzCkwfbsAJ12lq2ZjRwo7GRXdgjHsGIBiVkz6/p2RukIX9VJGM20qBCCofQBjs//DfPeJfpTe9vbRiog0gGWTLZOAM3F6k+oRyX2dDyDQJIxe3CMpRSrQGDKRDOI1yqTr0fzJI2Odr+GF9W4VP0plkwghETpgEC5FP0rNFSFajCDIzNcqv0UNyxyvvIiT/R/mZSZX/E9aa1Z8Mco+1dpqDKb0r1YMsW0e4KB9IMoHTJeP0bW6mfBv0zG6gMElWCC7dmnb/t3eL0YJIwbsxy2tJalw2Nibpd1LSKVYJozC9+jP7V8XuzW7MdoRAtcrr3W/tl4/ThF7wpP9P9HHCMDQrAte4R/nPqfnEIzkLwPRcSk+zbQzB5EOsBXNaYbZygHU/iqxvH5v2Fn7pOMdDyNEBJbJhlI3YcUEkumiLTP28a36E/uJWP1cqX2Gl3OVgZT91PyrzBaOdpOV65GLZwjbfVS5XD/IwAAB5BJREFUqo3hqyoSo/m9f4lOexjQGMJGChM/qiOFgSHseHUQsy5ZtyIS6ZBTpe+QNPP0JXa1fyYQ7e8XUTri7MLzDGcOYbTSkgKBIWwO9XyBUHnUwrlmvYhRwJQOUhhEyscUCXLWBlJmAYAn+7/SWoW0Z9IxXj/OeP0NNmcOEekANyox4Z4g4ecoB1NYIoEUBrVwDkNYCCGXmSG/m8HUQ/iqyqR4A6UjAu1SDabZkjlCqBtErfZ5rRUKH1vm8XT5pvuMifmgWLciUg1muOZf4nDHb+EY2eZYgsoPmXHPcKjni9e1kDdbznd1PMlQ6uC7unWbMYQJ9wRuWEQg6LA3trtBQ+VhSIukkSdlFFZ03VKEXKy+RHdiBwV7Cw1VxhA23c42bJkmYeTIWL0U7C1kzF6yVh+nS88xkLqPvD204tAmrTVF7wJ9yXsJVB03vEa+VWthySSOkSNpFqiFs7hRkVo4S1di5/v5646J+WezbgOrOaufZwb+MwW7mabVaC5Vf0rK7GqvNhZJGnmGUgeJtN8yLdZUwmlenv1LXpv7Gv3JvWxKPwgIQtVg3huj6F1hIRhH6YByMMm8f4mXZv6My7VXbwhg9iX30J/cS6Bdziw8x8bUPqQwCbWH0mHbkUsKA0ukeKv4t1TC2RXfl9aaQLu4UZHuxC4W/CuU/DFyVrPTuBpMUQkmccN5ehK7kcLElEm6nO00otJ7/nuOiblT1uVKJFAu17wL7apHADcqM9cYZXv2CDONs0BTWALVwJIJQuVxsvT37C98lt7Erlb5tcGjvb+DLdOt52gaqsLZ8vMMpR/AkRkO9/w2hrBROmR79jFsI8VStytITPZ3/jKBqnN64R9IGnkOdH0eW6bQWuPIDBmzhy5nmMu117hYfYnexG4GU/tXTuuiWPDH6E7sYq5xhpw1QNrs4UTxr+lJjlCwt7I99zQCwUzjNEPpw1yqHuVq7RUSRp6keXu+JzEx7zfrsk4kUgGl4CqWTLLYtn6y9G0CVWd/4bMsnuBT7klev/YNntz4FSyZQmuFY2RwZLZ18KotRDONs5yvvMDDPV/gdOm7nK+8yFMb/5CEsVRqXvKvUvKvsDnzcPt5vqox0zjLRP0EjpFhS+bQMqety9VXKDjDWDLJTOMM9bDI7o6nyJi9q9aGKEICVccUDkarojPUDbSOsGTTBrARFbFlBiksfFUhUC4JowNTrq1SNSbmfWDFgNy6XIkY0lrWV+FHNWYb7/Cx3t8hZ20Amv6dY9VX2JH7BAV7uReIRlP2J5ltnGuddIJIe/Qld3O+fJRGVCbUHlfrb7SECiLtca78A6pBczLelsxhANyoRM7qx86keWnmz+lLjCCFpBrMIoSkO7EDAVypHeNS9WU+NfhH2HJ1y0QhBAYWhrHcBMkSyzM6yVagF8AxcjhGPIgpZn2yLkXkerTWXKy+TKc9SN4eascrptxTFP3LPNL72zc8RyDImN3YqfSyFvIFf5Ifz/wpG1P7GOn4ebqcYbJWL1prTpS+yYbkPezZ8Ey7a1QIQd5uxir8Rh1DWPQldyEwmG6cYbL+Nk/0fxlT2lTDOTrsjTcVkJiYDyPrNrC6RFM0EkaOk6Vvc7n2KkX/Mq9e+xr7Op8lZRRWPGkNaZM0OzBFAkNYTNRP8Pzkf2VT+gEOdv1rhjOPMFo5Sqh8xuvHmai/RcHehCWTmHKlmozlxgFF/wobknuXBXmVvrUfSEzMh411vxIRQrIz9wSaCDcqMVk/yfcm/ghP1aiH8xT9MTrsASTLy8ubWRpF0RvjbPn7+KpO0sjTm9iNIUwSRo7B1P0cnf4T+hIjPLnxD27of7kepRWWTAECT1WZa5xnZ/YJ3KiEY2QwhU3OWn1KXkzMh5V1LyLQKt3WBkpHTLhvsTF1L/fk/wUT9Tc5Ov2/cWSaPflPMZQ+2DT11ZpSyyxHCMlIxzN02P38aOpP0CjcsMScdwGtI4Yzj3Jq4TtkrB42pR/ClitPs8/bg3y879+hdMgb1/6abmcbeXuQy7VXmHJPYckUWzKHUEQYGLyXs3ZjYtYz6zI7037xViajHEwxVn2Fa94FhjOPsDnzUFssPFXhVOm7vFX8W/Z1foYDhc8BtOz5jPY4R6Ujvjv+XwiUS9bqZXv2cTam9mEKmyv11/np7F+htWJDai+DqfsZzhxuZ2EWVzUlf5xTpW9jyQT7C5/FMTLNKXPRAqcXnuNE6Zvszj3Fg92/Fo83iPkwsuKVcV2KiNaKa94F5v3L1MN5DGHRk9hJt7N1xR4SrRUT7glmGufY3/lLNxjcLG5z7No3MKXDnvwvYInksupWN1pgtPJDvKjGSMfPkza7AKiGM8x7Y1SCaYQQ9CfvIW9varq1Lrt9Ulyuvcq58j/y+IbfW1ZRGxPzIeFuEhFNIypjCAtTOu2ajZtlPa5/H6vVZzSHVcsVRWalfSwehxACS6aQLbet1Y5jceX07vhMTMyHhLtHRGJiYtYlHwZTopiYmPVGLCIxMTF3RCwiMTExd8St6kTi6GBMTMxNiVciMTExd0QsIjExMXdELCIxMTF3RCwiMTExd0QsIjExMXdELCIxMTF3xP8H11zzEFuf2LgAAAAASUVORK5CYII=","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# 绘制词云\n","import matplotlib.pyplot as plt\n","from wordcloud import WordCloud\n","\n","\n","# 正面情感词词云\n","freq_pos = posdata.groupby('word')['word'].count()\n","freq_pos = freq_pos.sort_values(ascending = False)\n","backgroud_Image=plt.imread('./pl.jpg')\n","wordcloud = WordCloud(font_path=\"./simsun.ttc\",\n","                      max_words=100,\n","                      background_color='white',\n","                      mask=backgroud_Image)\n","pos_wordcloud = wordcloud.fit_words(freq_pos)\n","plt.imshow(pos_wordcloud)\n","plt.axis('off') \n","plt.show()\n","\n","\n","# 负面情感词词云\n","freq_neg = negdata.groupby(by = ['word'])['word'].count()\n","freq_neg = freq_neg.sort_values(ascending = False)\n","neg_wordcloud = wordcloud.fit_words(freq_neg)\n","plt.imshow(neg_wordcloud)\n","plt.axis('off') \n","plt.show()"]},{"cell_type":"code","execution_count":42,"metadata":{"collapsed":false,"id":"F8D7051CBA7C47D8AF6C7B6F3D39AE09","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 将结果写出,每条评论作为一行\n","posdata.to_csv(\"./posdata.csv\", index = False, encoding = 'utf-8')\n","negdata.to_csv(\"./negdata.csv\", index = False, encoding = 'utf-8')"]},{"cell_type":"markdown","metadata":{"id":"788E70CC6D8A48F087A64BFE518A4D7B","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["由图正面情感评论词云可知，“不错”“满意”“好评”等正面情感词出现的频数较高，并且没有掺杂负面情感词语，可以看出情感分析能较好地将正面情感评论抽取出来。  \n","\n","由图负面情感评论词云可知，“差评”“垃圾”“不好”“太差”等负面情感词出现的频数较高，并且没有掺杂正面情感词语，可以看出情感分析能较好地将负面情感评论抽取出来。  \n","\n","____  \n","\n","## LinearSVC模型预测情感  \n","将数据集划分为训练集和测试集(8:2)，通过TfidfVectorizer将评论文本向量化，在来训练LinearSVC模型，查看模型在训练集上的得分，预测测试集"]},{"cell_type":"code","execution_count":43,"metadata":{"collapsed":false,"id":"69B48809A5C746B38AB8C069BF8CA0E8","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>content</th>\n","      <th>creationTime</th>\n","      <th>nickname</th>\n","      <th>referenceName</th>\n","      <th>content_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什...</td>\n","      <td>2017-04-17 13:01:54</td>\n","      <td>鑫***辰</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>安装师傅很给力，热水器也好用，感谢美的。</td>\n","      <td>2017-04-17 10:45:33</td>\n","      <td>切***药</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>还没安装，基本满意</td>\n","      <td>2017-04-17 10:58:33</td>\n","      <td>j***x</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>电热水器收到了，京东自营商品就是好，发货速度快，品质有保障，安装效果好，宝贝非常喜欢，冬天可...</td>\n","      <td>2017-10-18 20:22:33</td>\n","      <td>j***2</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>用了几次才来评价，对产品非常满意，加热快保温时间长，售后服务特别好，主动打电话询问送货情况帮...</td>\n","      <td>2017-04-17 09:19:16</td>\n","      <td>j***6</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>pos</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                             content         creationTime   \n","0  东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什...  2017-04-17 13:01:54  \\\n","1                               安装师傅很给力，热水器也好用，感谢美的。  2017-04-17 10:45:33   \n","2                                          还没安装，基本满意  2017-04-17 10:58:33   \n","3  电热水器收到了，京东自营商品就是好，发货速度快，品质有保障，安装效果好，宝贝非常喜欢，冬天可...  2017-10-18 20:22:33   \n","4  用了几次才来评价，对产品非常满意，加热快保温时间长，售后服务特别好，主动打电话询问送货情况帮...  2017-04-17 09:19:16   \n","\n","  nickname                            referenceName content_type  \n","0    鑫***辰  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","1    切***药  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","2    j***x  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","3    j***2  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n","4    j***6  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  "]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["reviews.head()"]},{"cell_type":"code","execution_count":44,"metadata":{"collapsed":false,"id":"93B2E83B12D0488D857AEC9C68920374","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>content</th>\n","      <th>creationTime</th>\n","      <th>nickname</th>\n","      <th>referenceName</th>\n","      <th>content_type</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什...</td>\n","      <td>2017-04-17 13:01:54</td>\n","      <td>鑫***辰</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>安装师傅很给力，热水器也好用，感谢美的。</td>\n","      <td>2017-04-17 10:45:33</td>\n","      <td>切***药</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>还没安装，基本满意</td>\n","      <td>2017-04-17 10:58:33</td>\n","      <td>j***x</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>电热水器收到了，京东自营商品就是好，发货速度快，品质有保障，安装效果好，宝贝非常喜欢，冬天可...</td>\n","      <td>2017-10-18 20:22:33</td>\n","      <td>j***2</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>用了几次才来评价，对产品非常满意，加热快保温时间长，售后服务特别好，主动打电话询问送货情况帮...</td>\n","      <td>2017-04-17 09:19:16</td>\n","      <td>j***6</td>\n","      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                             content         creationTime   \n","0  东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什...  2017-04-17 13:01:54  \\\n","1                               安装师傅很给力，热水器也好用，感谢美的。  2017-04-17 10:45:33   \n","2                                          还没安装，基本满意  2017-04-17 10:58:33   \n","3  电热水器收到了，京东自营商品就是好，发货速度快，品质有保障，安装效果好，宝贝非常喜欢，冬天可...  2017-10-18 20:22:33   \n","4  用了几次才来评价，对产品非常满意，加热快保温时间长，售后服务特别好，主动打电话询问送货情况帮...  2017-04-17 09:19:16   \n","\n","  nickname                            referenceName  content_type  \n","0    鑫***辰  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)           1.0  \n","1    切***药  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)           1.0  \n","2    j***x  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)           1.0  \n","3    j***2  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)           1.0  \n","4    j***6  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)           1.0  "]},"execution_count":44,"metadata":{},"output_type":"execute_result"}],"source":["reviews['content_type'] = reviews['content_type'].map(lambda x:1.0 if x == 'pos' else 0.0)\n","reviews.head()"]},{"cell_type":"code","execution_count":45,"metadata":{"collapsed":false,"id":"A2C4A8EF064243B2ACD8B29BD54CD263","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["((1579,), (1579,), (395,), (395,))"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.feature_extraction.text import TfidfVectorizer as TFIDF  # 原始文本转化为tf-idf的特征矩阵\n","from sklearn.svm import LinearSVC\n","from sklearn.calibration import CalibratedClassifierCV\n","from sklearn.model_selection import train_test_split\n","\n","# 将有标签的数据集划分成训练集和测试集\n","train_X,valid_X,train_y,valid_y = train_test_split(reviews['content'],reviews['content_type'],test_size=0.2,random_state=42)\n","\n","train_X.shape,train_y.shape,valid_X.shape,valid_y.shape"]},{"cell_type":"code","execution_count":46,"metadata":{"collapsed":false,"id":"6A49D2A4973942C3A216C93FDDEE143D","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 模型构建\n","model_tfidf = TFIDF(min_df=5, max_features=5000, ngram_range=(1,3), use_idf=1, smooth_idf=1)\n","# 学习idf vector\n","model_tfidf.fit(train_X)\n","# 把文档转换成 X矩阵（该文档中该特征词出现的频次），行是文档个数，列是特征词的个数\n","train_vec = model_tfidf.transform(train_X)"]},{"cell_type":"code","execution_count":47,"metadata":{"collapsed":false,"id":"B664998BDD5B4FDF845FAA21EAF65BC6","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["array([[0.81757399, 0.52795963, 0.22985082, ..., 0.        , 0.        ,\n","        0.        ],\n","       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n","        0.        ],\n","       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n","        0.        ],\n","       ...,\n","       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n","        0.        ],\n","       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n","        0.        ],\n","       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n","        0.        ]])"]},"execution_count":47,"metadata":{},"output_type":"execute_result"}],"source":["train_vec.toarray()"]},{"cell_type":"code","execution_count":48,"metadata":{"collapsed":false,"id":"21E8D8F5CBD942EF97C246621400E949","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before 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relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CalibratedClassifierCV(estimator=LinearSVC())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CalibratedClassifierCV</label><div class=\"sk-toggleable__content\"><pre>CalibratedClassifierCV(estimator=LinearSVC())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC()</pre></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["CalibratedClassifierCV(estimator=LinearSVC())"]},"execution_count":48,"metadata":{},"output_type":"execute_result"}],"source":["# 模型训练\n","model_SVC = LinearSVC()\n","clf = CalibratedClassifierCV(model_SVC)\n","clf.fit(train_vec,train_y)"]},{"cell_type":"code","execution_count":49,"metadata":{"collapsed":false,"id":"169709D893754A9884653124210ECDFB","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["array([[0.58764042, 0.41235958],\n","       [0.58764042, 0.41235958],\n","       [0.03818522, 0.96181478],\n","       [0.10191849, 0.89808151],\n","       [0.9450088 , 0.0549912 ]])"]},"execution_count":49,"metadata":{},"output_type":"execute_result"}],"source":["# 把文档转换成矩阵\n","valid_vec = model_tfidf.transform(valid_X)\n","# 验证\n","pre_valid = clf.predict_proba(valid_vec)\n","pre_valid[:5]"]},{"cell_type":"code","execution_count":50,"metadata":{"collapsed":false,"id":"1385EF24AA8A4BCE80BEE684306472F1","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["正例: 76\n","负例: 319\n"]}],"source":["pre_valid = clf.predict(valid_vec)\n","print('正例:',sum(pre_valid == 1))\n","print('负例:',sum(pre_valid == 0))"]},{"cell_type":"code","execution_count":51,"metadata":{"collapsed":false,"id":"D1C802695DB64320AD7BEA5412EF3FF8","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["准确率: 0.6683544303797468\n"]}],"source":["from sklearn.metrics import accuracy_score\n","\n","score = accuracy_score(pre_valid,valid_y)\n","print(\"准确率:\",score)"]},{"cell_type":"markdown","metadata":{"id":"D3C95D238755409F8ED65FB96EB72BE9","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["## LDA模型  \n","\n","LDA是一种文档主题生成模型，包含词、主题和文档三层结构。  \n","\n","1. 主题模型在自然语言处理等领域是用来在一系列文档中发现抽象主题的一种统计模型。判断两个文档相似性的传统方法是通过查看两个文档共同出现的单词的多少，如TF（词频）、TF-IDF（词频—逆向文档频率）等，这种方法没有考虑文字背后的语义关联，例如，两个文档共同出现的单词很少甚至没有，但两个文档是相似的，因此在判断文档相似性时，需要使用主题模型进行语义分析并判断文档相似性。如果一篇文档有多个主题，则一些特定的可代表不同主题的词语就会反复出现，此时，运用主题模型，能够发现文本中使用词语的规律，并且把规律相似的文本联系到一起，以寻求非结构化的文本集中的有用信息。例如，在美的电热水器的商品评论文本数据中，代表电热水器特征的词语如“安装”“出水量”“服务”等会频繁地出现在评论中，运用主题模型，把热水器代表性特征相关的情感描述性词语与对应特征的词语联系起来，从而深入了解用户对电热水器的关注点及用户对于某一特征的情感倾向  \n","\n","\n","\n","2. LDA主题模型潜在狄利克雷分配，即LDA模型（Latent Dirichlet Allocation，LDA）是由Blei等人在2003年提出的生成式主题模型。所谓生成模型，就是说，我们认为一篇文章的每个词都是通过“以一定概率选择了某个主题，并从这个主题中以一定概率选择某个词语”这样一个过程得到。文档到主题服从多项式分布，主题到词服从多项式分布。LDA模型也被称为3层贝叶斯概率模型，包含文档（d）、主题（z）、词（w）3层结构，能够有效对文本进行建模，和传统的空间向量模型（VSM）相比，增加了概率的信息。通过LDA主题模型，能够挖掘数据集中的潜在主题，进而分析数据集的集中关注点及其相关特征词。LDA模型采用词袋模型（Bag of Words，BOW）将每一篇文档视为一个词频向量，从而将文本信息转化为易于建模的数字信息。定义词表大小为L，一个L维向量（1，0，0，…，0，0）表示一个词。由N个词构成的评论记为d=（w1，w2，…，wN）。假设某一商品的评论集D由M篇评论构成，记为D=（d1，d2，…，dM）。M篇评论分布着K个主题，记为Zi=（i=1，2，…，K）。记a和b为狄利克雷函数的先验参数，q为主题在文档中的多项分布的参数，其服从超参数为a的Dirichlet先验分布，f为词在主题中的多项分布的参数，其服从超参数b的Dirichlet先验分布。"]},{"cell_type":"code","execution_count":52,"metadata":{"collapsed":false,"id":"4FAECB06C8CB4FFE8012CA732860835C","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["import re\n","import itertools\n","\n","from gensim import corpora, models\n","\n","\n","# 载入情感分析后的数据\n","posdata = pd.read_csv(\"./posdata.csv\", encoding = 'utf-8')\n","negdata = pd.read_csv(\"./negdata.csv\", encoding = 'utf-8')\n","\n","\n","# 建立词典\n","pos_dict = corpora.Dictionary([[i] for i in posdata['word']])  # 正面\n","neg_dict = corpora.Dictionary([[i] for i in negdata['word']])  # 负面\n","\n","# 建立语料库\n","pos_corpus = [pos_dict.doc2bow(j) for j in [[i] for i in posdata['word']]]  # 正面\n","neg_corpus = [neg_dict.doc2bow(j) for j in [[i] for i in negdata['word']]]   # 负面"]},{"cell_type":"markdown","metadata":{"id":"11463888AF7F492F8B5FB992DB614019","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["### 主题数寻优  \n","\n","基于相似度的自适应最优LDA模型选择方法，确定主题数并进行主题分析。实验证明该方法可以在不需要人工调试主题数目的情况下，用相对少的迭代找到最优的主题结构。  \n","\n","具体步骤如下：  \n","1. 取初始主题数k值，得到初始模型，计算各主题之间的相似度（平均余弦距离）。  \n","2. 增加或减少k值，重新训练模型，再次计算各主题之间的相似度。  \n","3. 重复步骤2直到得到最优k值。"]},{"cell_type":"code","execution_count":53,"metadata":{"collapsed":false,"id":"C34D7F0D2F48443688FE0B27BEDD747C","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 余弦相似度函数\n","def cos(vector1, vector2):\n","    dot_product = 0.0;  \n","    normA = 0.0;  \n","    normB = 0.0;  \n","    for a,b in zip(vector1, vector2): \n","        dot_product += a*b  \n","        normA += a**2  \n","        normB += b**2  \n","    if normA == 0.0 or normB==0.0:  \n","        return(None)  \n","    else:  \n","        return(dot_product / ((normA*normB)**0.5))   \n","\n","# 主题数寻优\n","def lda_k(x_corpus, x_dict):  \n","    \n","    # 初始化平均余弦相似度\n","    mean_similarity = []\n","    mean_similarity.append(1)\n","    \n","    # 循环生成主题并计算主题间相似度\n","    for i in np.arange(2,11):\n","        # LDA模型训练\n","        lda = models.LdaModel(x_corpus, num_topics = i, id2word = x_dict)\n","        for j in np.arange(i):\n","            term = lda.show_topics(num_words = 50)\n","            \n","        # 提取各主题词\n","        top_word = []\n","        for k in np.arange(i):\n","            top_word.append([''.join(re.findall('\"(.*)\"',i)) \\\n","                             for i in term[k][1].split('+')])  # 列出所有词\n","           \n","        # 构造词频向量\n","        word = sum(top_word,[])  # 列出所有的词   \n","        unique_word = set(word)  # 去除重复的词\n","        \n","        # 构造主题词列表，行表示主题号，列表示各主题词\n","        mat = []\n","        for j in np.arange(i):\n","            top_w = top_word[j]\n","            mat.append(tuple([top_w.count(k) for k in unique_word]))  \n","            \n","        p = list(itertools.permutations(list(np.arange(i)),2))\n","        l = len(p)\n","        top_similarity = [0]\n","        for w in np.arange(l):\n","            vector1 = mat[p[w][0]]\n","            vector2 = mat[p[w][1]]\n","            top_similarity.append(cos(vector1, vector2))\n","            \n","        # 计算平均余弦相似度\n","        mean_similarity.append(sum(top_similarity)/l)\n","    return(mean_similarity)"]},{"cell_type":"code","execution_count":54,"metadata":{"collapsed":false,"id":"DA33D34B1A8140088B76FBB67F9E30AC","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 计算主题平均余弦相似度\n","pos_k = lda_k(pos_corpus, pos_dict)\n","neg_k = lda_k(neg_corpus, neg_dict)"]},{"cell_type":"code","execution_count":56,"metadata":{"collapsed":false,"id":"81C11CCFFACC4493B2DC48AB9B05AEFB","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 720x576 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# 绘制主题平均余弦相似度图形\n","from matplotlib.font_manager import FontProperties  \n","font = FontProperties(size=14)\n","plt.rcParams[\"font.sans-serif\"]=[\"SimHei\"] #设置字体\n","\n","fig = plt.figure(figsize=(10,8))\n","ax1 = fig.add_subplot(211)\n","ax1.plot(pos_k)\n","ax1.set_xlabel('正面评论LDA主题数寻优', fontproperties=font)\n","\n","ax2 = fig.add_subplot(212)\n","ax2.plot(neg_k)\n","ax2.set_xlabel('负面评论LDA主题数寻优', fontproperties=font)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"59E9522CD6414B7FA4FD2823E1C1C47C","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["由图可知，对于正面评论数据，当主题数为2或3时，主题间的平均余弦相似度就达到了最低。因此，对正面评论数据做LDA，可以选择主题数为3；对于负面评论数据，当主题数为3时，主题间的平均余弦相似度也达到了最低。因此，对负面评论数据做LDA，也可以选择主题数为3。  \n","\n","----  \n","\n","### 评价主题分析结果  \n","\n","根据主题数寻优结果，使用Python的Gensim模块对正面评论数据和负面评论数据分别构建LDA主题模型，设置主题数为3，经过LDA主题分析后，每个主题下生成10个最有可能出现的词语以及相应的概率"]},{"cell_type":"code","execution_count":57,"metadata":{"collapsed":false,"id":"192EB1A8C23241628165744F6C39268B","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# LDA主题分析\n","pos_lda = models.LdaModel(pos_corpus, num_topics = 3, id2word = pos_dict)  \n","neg_lda = models.LdaModel(neg_corpus, num_topics = 3, id2word = neg_dict)"]},{"cell_type":"code","execution_count":58,"metadata":{"collapsed":false,"id":"E5A9733E690B40CD8294878E5D5ED7C2","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["[(0,\n","  '0.046*\"售后服务\" + 0.045*\"超级\" + 0.025*\"服务\" + 0.015*\"收费\" + 0.014*\"安装费\" + 0.011*\"产品\" + 0.011*\"品牌\" + 0.011*\"钱\" + 0.011*\"速度\" + 0.009*\"服务态度\"'),\n"," (1,\n","  '0.149*\"安装\" + 0.047*\"差\" + 0.028*\"不错\" + 0.022*\"免费\" + 0.017*\"客服\" + 0.016*\"人员\" + 0.016*\"东西\" + 0.013*\"太\" + 0.010*\"花\" + 0.010*\"物流\"'),\n"," (2,\n","  '0.043*\"师傅\" + 0.033*\"售后\" + 0.029*\"满意\" + 0.025*\"送货\" + 0.017*\"电话\" + 0.017*\"很快\" + 0.015*\"装\" + 0.013*\"值得\" + 0.013*\"质量\" + 0.013*\"好评\"')]"]},"execution_count":58,"metadata":{},"output_type":"execute_result"}],"source":["pos_lda.print_topics(num_words = 10)"]},{"cell_type":"code","execution_count":59,"metadata":{"collapsed":false,"id":"AA161DD6FBF1484C81429292C272ADB4","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["[(0,\n","  '0.121*\"安装\" + 0.050*\"太\" + 0.038*\"评\" + 0.018*\"服务\" + 0.018*\"不好\" + 0.017*\"质量\" + 0.013*\"产品\" + 0.012*\"材料费\" + 0.012*\"贵\" + 0.012*\"坑人\"'),\n"," (1,\n","  '0.026*\"安装费\" + 0.023*\"东西\" + 0.023*\"售后\" + 0.018*\"打电话\" + 0.014*\"货\" + 0.013*\"遥控器\" + 0.009*\"时\" + 0.009*\"特别\" + 0.009*\"问\" + 0.009*\"漏水\"'),\n"," (2,\n","  '0.077*\"差\" + 0.034*\"垃圾\" + 0.029*\"师傅\" + 0.024*\"客服\" + 0.024*\"装\" + 0.019*\"收\" + 0.018*\"加热\" + 0.015*\"换\" + 0.015*\"坏\" + 0.014*\"太慢\"')]"]},"execution_count":59,"metadata":{},"output_type":"execute_result"}],"source":["neg_lda.print_topics(num_words = 10)"]},{"cell_type":"markdown","metadata":{"id":"02AC12986B954934B7875C510BF0BA4A","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["## 可视化模型训练结果"]},{"cell_type":"code","execution_count":60,"metadata":{"collapsed":false,"id":"B8E46AA74681462B989B92A1CD629FCD","jupyter":{},"notebookId":"64474060ff981dedde47fe4c","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"ename":"AttributeError","evalue":"module 'pyLDAvis' has no attribute 'gensim'","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[1;32md:\\project\\git\\my_pda\\other_project\\good_reviews\\电商产品评论数据情感分析.ipynb Cell 67\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#Y123sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpyLDAvis\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#Y123sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m vis \u001b[39m=\u001b[39m pyLDAvis\u001b[39m.\u001b[39;49mgensim\u001b[39m.\u001b[39mprepare(pos_lda,pos_corpus,pos_dict)\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#Y123sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39m# 需要的三个参数都可以从硬盘读取的，前面已经存储下来了\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#Y123sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#Y123sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39m# 在浏览器中心打开一个界面\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#Y123sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39m# pyLDAvis.show(vis)\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#Y123sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m \n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#Y123sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39m# 在notebook的output cell中显示\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/d%3A/project/git/my_pda/other_project/good_reviews/%E7%94%B5%E5%95%86%E4%BA%A7%E5%93%81%E8%AF%84%E8%AE%BA%E6%95%B0%E6%8D%AE%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.ipynb#Y123sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m pyLDAvis\u001b[39m.\u001b[39mdisplay(vis)\n","\u001b[1;31mAttributeError\u001b[0m: module 'pyLDAvis' has no attribute 'gensim'"]}],"source":["import pyLDAvis\n","\n","vis = pyLDAvis.gensim.prepare(pos_lda,pos_corpus,pos_dict)\n","# 需要的三个参数都可以从硬盘读取的，前面已经存储下来了\n","\n","# 在浏览器中心打开一个界面\n","# pyLDAvis.show(vis)\n","\n","# 在notebook的output cell中显示\n","pyLDAvis.display(vis)"]},{"cell_type":"code","execution_count":63,"metadata":{},"outputs":[],"source":["import pyLDAvis.gensim_models as gensimvis\n","from pyLDAvis import gensim\n","\n","\n","pyLDAvis.enable_notebook()\n","\n","'''\n","lda: 计算好的话题模型\n","\n","corpus: 文档词频矩阵\n","\n","dictionary: 词语空间\n","'''\n","d = gensim.prepare(pos_lda,pos_corpus,pos_dict)\n","d = gensimvis.prepare(pos_lda,pos_corpus,pos_dict)\n","# pyLDAvis.display(d)\t\t    # 展示在浏览器\n","pyLDAvis.save_html(d, 'lda.html')\n"]},{"cell_type":"markdown","metadata":{"id":"EF068BC64BA74280B4D54E3AB1D1D296","jupyter":{},"mdEditEnable":false,"notebookId":"64474060ff981dedde47fe4c","runtime":{"execution_status":null,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"source":["综合以上对主题及其中的高频特征词的分析得出，美的电热水器有价格实惠、性价比高、外观好看、服务好等优势。相对而言，用户对美的电热水器的抱怨点主要体现在安装的费用高及售后服务差等方面。因此，用户的购买原因可以总结为以下几个方面：美的是大品牌值得信赖、美的电热水器价格实惠、性价比高。  \n","\n","根据对京东平台上美的电热水器的用户评价情况进行LDA主题模型分析，对美的品牌提出以下两点建议：  \n","1. 在保持热水器使用方便、价格实惠等优点的基础上，对热水器进行加热功能上的改进，从整体上提升热水器的质量。  \n","2. 提升安装人员及客服人员的整体素质，提高服务质量，注重售后服务。建立安装费用收取的明文细则，并进行公布，以减少安装过程中乱收费的现象。适度降低安装费用和材料费用，以此在大品牌的竞争中凸显优势。  \n","\n","## 参考资料  \n","[Python数据分析与挖掘实战](https://book.douban.com/subject/34888317/)"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.12"},"vscode":{"interpreter":{"hash":"c261aea317cc0286b3b3261fbba9abdec21eaa57589985bb7a274bf54d6cc0a7"}}},"nbformat":4,"nbformat_minor":2}
